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              <p class="caption"><span class="caption-text">Notes</span></p>
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<h1 id="index">Index</h1>

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<h2 id="_">_</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Normalize.__call__">__call__() (torchvision.transforms.Normalize method)</a>

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        <li><a href="torchvision/transforms.html#torchvision.transforms.ToPILImage.__call__">(torchvision.transforms.ToPILImage method)</a>
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      <li><a href="torchvision/datasets.html#torchvision.datasets.CIFAR10.__getitem__">__getitem__() (torchvision.datasets.CIFAR10 method)</a>

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        <li><a href="torchvision/datasets.html#torchvision.datasets.CocoDetection.__getitem__">(torchvision.datasets.CocoDetection method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.DatasetFolder.__getitem__">(torchvision.datasets.DatasetFolder method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.Flickr30k.__getitem__">(torchvision.datasets.Flickr30k method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.Flickr8k.__getitem__">(torchvision.datasets.Flickr8k method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.LSUN.__getitem__">(torchvision.datasets.LSUN method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.PhotoTour.__getitem__">(torchvision.datasets.PhotoTour method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.STL10.__getitem__">(torchvision.datasets.STL10 method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.SVHN.__getitem__">(torchvision.datasets.SVHN method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.USPS.__getitem__">(torchvision.datasets.USPS method)</a>
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        <li><a href="torchvision/datasets.html#torchvision.datasets.VOCDetection.__getitem__">(torchvision.datasets.VOCDetection method)</a>
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      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.__init__">__init__() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
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      <li><a href="autograd.html#torch.autograd.function._ContextMethodMixin">_ContextMethodMixin (class in torch.autograd.function)</a>
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      <li><a href="sparse.html#torch.sparse.FloatTensor._indices">_indices() (torch.sparse.FloatTensor method)</a>
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      <li><a href="sparse.html#torch.sparse.FloatTensor._nnz">_nnz() (torch.sparse.FloatTensor method)</a>
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      <li><a href="sparse.html#torch.sparse.FloatTensor._values">_values() (torch.sparse.FloatTensor method)</a>
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<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.abs">abs() (in module torch)</a>

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        <li><a href="tensors.html#torch.Tensor.abs">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.abs_">abs_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.AbsTransform">AbsTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="torch.html#torch.acos">acos() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.acos">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.acos_">acos_() (torch.Tensor method)</a>
</li>
      <li><a href="optim.html#torch.optim.Adadelta">Adadelta (class in torch.optim)</a>
</li>
      <li><a href="optim.html#torch.optim.Adagrad">Adagrad (class in torch.optim)</a>
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      <li><a href="optim.html#torch.optim.Adam">Adam (class in torch.optim)</a>
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      <li><a href="optim.html#torch.optim.Adamax">Adamax (class in torch.optim)</a>
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      <li><a href="optim.html#torch.optim.AdamW">AdamW (class in torch.optim)</a>
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      <li><a href="nn.functional.html#torch.nn.functional.adaptive_avg_pool1d">adaptive_avg_pool1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.adaptive_avg_pool2d">adaptive_avg_pool2d() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.adaptive_avg_pool2d">(in module torch.nn.quantized.functional)</a>
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      <li><a href="nn.functional.html#torch.nn.functional.adaptive_avg_pool3d">adaptive_avg_pool3d() (in module torch.nn.functional)</a>
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      <li><a href="nn.functional.html#torch.nn.functional.adaptive_max_pool1d">adaptive_max_pool1d() (in module torch.nn.functional)</a>
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      <li><a href="nn.functional.html#torch.nn.functional.adaptive_max_pool2d">adaptive_max_pool2d() (in module torch.nn.functional)</a>
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      <li><a href="nn.functional.html#torch.nn.functional.adaptive_max_pool3d">adaptive_max_pool3d() (in module torch.nn.functional)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveAvgPool1d">AdaptiveAvgPool1d (class in torch.nn)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveAvgPool2d">AdaptiveAvgPool2d (class in torch.nn)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss">AdaptiveLogSoftmaxWithLoss (class in torch.nn)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveMaxPool1d">AdaptiveMaxPool1d (class in torch.nn)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveMaxPool2d">AdaptiveMaxPool2d (class in torch.nn)</a>
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      <li><a href="nn.html#torch.nn.AdaptiveMaxPool3d">AdaptiveMaxPool3d (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.add">add() (in module torch)</a>, <a href="torch.html#torch.add">[1]</a>, <a href="torch.html#torch.add">[2]</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.add">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.add">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.add_">add_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.add_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_audio">add_audio() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars">add_custom_scalars() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_embedding">add_embedding() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_figure">add_figure() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_graph">add_graph() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_histogram">add_histogram() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_hparams">add_hparams() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_image">add_image() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_images">add_images() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_mesh">add_mesh() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.add_module">add_module() (torch.nn.Module method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.add_observer_">add_observer_() (in module torch.quantization)</a>
</li>
      <li><a href="optim.html#torch.optim.Optimizer.add_param_group">add_param_group() (torch.optim.Optimizer method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve">add_pr_curve() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.add_pruning_method">add_pruning_method() (torch.nn.utils.prune.PruningContainer method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.add_quant_dequant">add_quant_dequant() (in module torch.quantization)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_scalar">add_scalar() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_scalars">add_scalars() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_text">add_text() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_video">add_video() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="torch.html#torch.addbmm">addbmm() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.addbmm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addbmm_">addbmm_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.addcdiv">addcdiv() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.addcdiv">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addcdiv_">addcdiv_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.addcmul">addcmul() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.addcmul">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addcmul_">addcmul_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.addmm">addmm() (in module torch)</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.addmm">(in module torch.sparse)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.addmm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addmm_">addmm_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.addmv">addmv() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.addmv">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addmv_">addmv_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.addr">addr() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.addr">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.addr_">addr_() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.adjust_brightness">adjust_brightness() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.adjust_contrast">adjust_contrast() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.adjust_gamma">adjust_gamma() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.adjust_hue">adjust_hue() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.adjust_saturation">adjust_saturation() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.affine">affine() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.affine_grid">affine_grid() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.AffineTransform">AffineTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.alexnet">alexnet() (in module torchvision.models)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.align_as">align_as() (torch.Tensor method)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.align_to">align_to() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.BoolTensor.all">all() (torch.BoolTensor method)</a>
</li>
      <li><a href="distributed.html#torch.distributed.all_gather">all_gather() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.all_gather_multigpu">all_gather_multigpu() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.all_reduce">all_reduce() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.all_reduce_multigpu">all_reduce_multigpu() (in module torch.distributed)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.allclose">allclose() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.allclose">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.alpha_dropout">alpha_dropout() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.AlphaDropout">AlphaDropout (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.angle">angle() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.angle">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.BoolTensor.any">any() (torch.BoolTensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleList.append">append() (torch.nn.ModuleList method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterList.append">(torch.nn.ParameterList method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Module.apply">apply() (torch.nn.Module method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod.apply">(torch.nn.utils.prune.BasePruningMethod class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.CustomFromMask.apply">(torch.nn.utils.prune.CustomFromMask class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.Identity.apply">(torch.nn.utils.prune.Identity class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.L1Unstructured.apply">(torch.nn.utils.prune.L1Unstructured class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.LnStructured.apply">(torch.nn.utils.prune.LnStructured class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.apply">(torch.nn.utils.prune.PruningContainer class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomStructured.apply">(torch.nn.utils.prune.RandomStructured class method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomUnstructured.apply">(torch.nn.utils.prune.RandomUnstructured class method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.apply_">apply_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod.apply_mask">apply_mask() (torch.nn.utils.prune.BasePruningMethod method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.CustomFromMask.apply_mask">(torch.nn.utils.prune.CustomFromMask method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.Identity.apply_mask">(torch.nn.utils.prune.Identity method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.L1Unstructured.apply_mask">(torch.nn.utils.prune.L1Unstructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.LnStructured.apply_mask">(torch.nn.utils.prune.LnStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.apply_mask">(torch.nn.utils.prune.PruningContainer method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomStructured.apply_mask">(torch.nn.utils.prune.RandomStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomUnstructured.apply_mask">(torch.nn.utils.prune.RandomUnstructured method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.arange">arange() (in module torch)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.arg_constraints">arg_constraints (torch.distributions.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.arg_constraints">(torch.distributions.beta.Beta attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.arg_constraints">(torch.distributions.binomial.Binomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.arg_constraints">(torch.distributions.categorical.Categorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.arg_constraints">(torch.distributions.cauchy.Cauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.chi2.Chi2.arg_constraints">(torch.distributions.chi2.Chi2 attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.arg_constraints">(torch.distributions.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.arg_constraints">(torch.distributions.exponential.Exponential attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.arg_constraints">(torch.distributions.fishersnedecor.FisherSnedecor attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.arg_constraints">(torch.distributions.gamma.Gamma attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.arg_constraints">(torch.distributions.geometric.Geometric attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.arg_constraints">(torch.distributions.gumbel.Gumbel attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.arg_constraints">(torch.distributions.half_cauchy.HalfCauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.arg_constraints">(torch.distributions.half_normal.HalfNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.arg_constraints">(torch.distributions.independent.Independent attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.arg_constraints">(torch.distributions.laplace.Laplace attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.arg_constraints">(torch.distributions.log_normal.LogNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.arg_constraints">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.arg_constraints">(torch.distributions.multinomial.Multinomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.arg_constraints">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.arg_constraints">(torch.distributions.negative_binomial.NegativeBinomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.arg_constraints">(torch.distributions.normal.Normal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.arg_constraints">(torch.distributions.one_hot_categorical.OneHotCategorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.arg_constraints">(torch.distributions.pareto.Pareto attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.arg_constraints">(torch.distributions.poisson.Poisson attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.arg_constraints">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.arg_constraints">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.arg_constraints">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.arg_constraints">(torch.distributions.studentT.StudentT attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.arg_constraints">(torch.distributions.transformed_distribution.TransformedDistribution attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.arg_constraints">(torch.distributions.uniform.Uniform attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.arg_constraints">(torch.distributions.weibull.Weibull attribute)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.arg_constraints">arg_constraints() (torch.distributions.distribution.Distribution property)</a>
</li>
      <li><a href="torch.html#torch.argmax">argmax() (in module torch)</a>, <a href="torch.html#torch.argmax">[1]</a>, <a href="torch.html#torch.argmax">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.argmax">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.argmin">argmin() (in module torch)</a>, <a href="torch.html#torch.argmin">[1]</a>, <a href="torch.html#torch.argmin">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.argmin">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.argsort">argsort() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.argsort">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.as_strided">as_strided() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.as_strided">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.as_tensor">as_tensor() (in module torch)</a>
</li>
      <li><a href="optim.html#torch.optim.ASGD">ASGD (class in torch.optim)</a>
</li>
      <li><a href="torch.html#torch.asin">asin() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.asin">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.asin_">asin_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.atan">atan() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.atan">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.atan2">atan2() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.atan2">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.atan2_">atan2_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.atan_">atan_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.avg_pool1d">avg_pool1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.avg_pool2d">avg_pool2d() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.avg_pool2d">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.avg_pool3d">avg_pool3d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.AvgPool1d">AvgPool1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.AvgPool2d">AvgPool2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.AvgPool3d">AvgPool3d (class in torch.nn)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="B">B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="distributed.html#torch.distributed.Backend">Backend (class in torch.distributed)</a>
</li>
      <li><a href="autograd.html#torch.autograd.backward">backward() (in module torch.autograd)</a>

      <ul>
        <li><a href="rpc.html#torch.distributed.autograd.backward">(in module torch.distributed.autograd)</a>
</li>
        <li><a href="autograd.html#torch.autograd.Function.backward">(torch.autograd.Function static method)</a>
</li>
        <li><a href="autograd.html#torch.Tensor.backward">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.baddbmm">baddbmm() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.baddbmm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.baddbmm_">baddbmm_() (torch.Tensor method)</a>
</li>
      <li><a href="distributed.html#torch.distributed.barrier">barrier() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.bartlett_window">bartlett_window() (in module torch)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod">BasePruningMethod (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.batch_norm">batch_norm() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.batch_shape">batch_shape() (torch.distributions.distribution.Distribution property)</a>
</li>
      <li><a href="nn.html#torch.nn.BatchNorm1d">BatchNorm1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.BatchNorm2d">BatchNorm2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.BatchNorm3d">BatchNorm3d (class in torch.nn)</a>
</li>
      <li><a href="data.html#torch.utils.data.BatchSampler">BatchSampler (class in torch.utils.data)</a>
</li>
      <li><a href="nn.html#torch.nn.BCELoss">BCELoss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.BCEWithLogitsLoss">BCEWithLogitsLoss (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli">Bernoulli (class in torch.distributions.bernoulli)</a>
</li>
      <li><a href="torch.html#torch.bernoulli">bernoulli() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bernoulli">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.bernoulli_">bernoulli_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.beta.Beta">Beta (class in torch.distributions.beta)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.bfloat16">bfloat16() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bfloat16">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Bilinear">Bilinear (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.bilinear">bilinear() (in module torch.nn.functional)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.functional.html#torch.nn.functional.binary_cross_entropy">binary_cross_entropy() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.binary_cross_entropy_with_logits">binary_cross_entropy_with_logits() (in module torch.nn.functional)</a>
</li>
      <li><a href="torch.html#torch.bincount">bincount() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bincount">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.binomial.Binomial">Binomial (class in torch.distributions.binomial)</a>
</li>
      <li><a href="torch.html#torch.bitwise_not">bitwise_not() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bitwise_not">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.bitwise_not_">bitwise_not_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.bitwise_xor">bitwise_xor() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bitwise_xor">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.bitwise_xor_">bitwise_xor_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.blackman_window">blackman_window() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.bmm">bmm() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bmm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.bool">bool() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.bool">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.BoolTensor">BoolTensor (class in torch)</a>
</li>
      <li><a href="tensors.html#torch.BoolTensor.all">BoolTensor.all() (in module torch)</a>, <a href="tensors.html#torch.BoolTensor.all">[1]</a>
</li>
      <li><a href="tensors.html#torch.BoolTensor.any">BoolTensor.any() (in module torch)</a>, <a href="tensors.html#torch.BoolTensor.any">[1]</a>
</li>
      <li><a href="cuda.html#torch.cuda.comm.broadcast">broadcast() (in module torch.cuda.comm)</a>

      <ul>
        <li><a href="distributed.html#torch.distributed.broadcast">(in module torch.distributed)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.comm.broadcast_coalesced">broadcast_coalesced() (in module torch.cuda.comm)</a>
</li>
      <li><a href="distributed.html#torch.distributed.broadcast_multigpu">broadcast_multigpu() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.broadcast_tensors">broadcast_tensors() (in module torch)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.buffers">buffers() (torch.nn.Module method)</a>
</li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.BuildExtension">BuildExtension() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.byte">byte() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.byte">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="C">C</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.init.html#torch.nn.init.calculate_gain">calculate_gain() (in module torch.nn.init)</a>
</li>
      <li><a href="torch.html#torch.can_cast">can_cast() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.cartesian_prod">cartesian_prod() (in module torch)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.cat">cat (in module torch.distributions.constraints)</a>
</li>
      <li><a href="torch.html#torch.cat">cat() (in module torch)</a>
</li>
      <li><a href="distributions.html#torch.distributions.categorical.Categorical">Categorical (class in torch.distributions.categorical)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.CatTransform">CatTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.cauchy.Cauchy">Cauchy (class in torch.distributions.cauchy)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.cauchy_">cauchy_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.cdf">cdf() (torch.distributions.cauchy.Cauchy method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.cdf">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.cdf">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.cdf">(torch.distributions.half_cauchy.HalfCauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.cdf">(torch.distributions.half_normal.HalfNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.cdf">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.cdf">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.cdf">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.cdf">(torch.distributions.uniform.Uniform method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.cdist">cdist() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.ceil">ceil() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ceil">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.ceil_">ceil_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.CELU">CELU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.celu">celu() (in module torch.nn.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.center_crop">center_crop() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.CenterCrop">CenterCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.chain_matmul">chain_matmul() (in module torch)</a>
</li>
      <li><a href="data.html#torch.utils.data.ChainDataset">ChainDataset (class in torch.utils.data)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.char">char() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.char">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.constraints.Constraint.check">check() (torch.distributions.constraints.Constraint method)</a>
</li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.check_compiler_abi_compatibility">check_compiler_abi_compatibility() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="checkpoint.html#torch.utils.checkpoint.checkpoint">checkpoint() (in module torch.utils.checkpoint)</a>
</li>
      <li><a href="checkpoint.html#torch.utils.checkpoint.checkpoint_sequential">checkpoint_sequential() (in module torch.utils.checkpoint)</a>
</li>
      <li><a href="distributions.html#torch.distributions.chi2.Chi2">Chi2 (class in torch.distributions.chi2)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.children">children() (torch.nn.Module method)</a>
</li>
      <li><a href="torch.html#torch.cholesky">cholesky() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cholesky">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.cholesky_inverse">cholesky_inverse() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cholesky_inverse">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.cholesky_solve">cholesky_solve() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cholesky_solve">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.chunk">chunk() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.chunk">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.CIFAR10">CIFAR10 (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.CIFAR100">CIFAR100 (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.Cityscapes">Cityscapes (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.clamp">clamp() (in module torch)</a>, <a href="torch.html#torch.clamp">[1]</a>, <a href="torch.html#torch.clamp">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.clamp">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.clamp_">clamp_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict.clear">clear() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.clear">(torch.nn.ParameterDict method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.utils.clip_grad_norm_">clip_grad_norm_() (in module torch.nn.utils)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.clip_grad_value_">clip_grad_value_() (in module torch.nn.utils)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.clone">clone() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.clone">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.clone">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.close">close() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.coalesce">coalesce() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.CocoCaptions">CocoCaptions (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.CocoDetection">CocoDetection (class in torchvision.datasets)</a>
</li>
      <li><a href="jit.html#torch.jit.ScriptModule.code">code() (torch.jit.ScriptModule property)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.ColorJitter">ColorJitter (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.combinations">combinations() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.compiled_with_cxx11_abi">compiled_with_cxx11_abi() (in module torch)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Compose">Compose (class in torchvision.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.ComposeTransform">ComposeTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod.compute_mask">compute_mask() (torch.nn.utils.prune.BasePruningMethod method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.LnStructured.compute_mask">(torch.nn.utils.prune.LnStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.compute_mask">(torch.nn.utils.prune.PruningContainer method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomStructured.compute_mask">(torch.nn.utils.prune.RandomStructured method)</a>
</li>
      </ul></li>
      <li><a href="data.html#torch.utils.data.ConcatDataset">ConcatDataset (class in torch.utils.data)</a>
</li>
      <li><a href="distributions.html#torch.distributions.beta.Beta.concentration0">concentration0() (torch.distributions.beta.Beta property)</a>
</li>
      <li><a href="distributions.html#torch.distributions.beta.Beta.concentration1">concentration1() (torch.distributions.beta.Beta property)</a>
</li>
      <li><a href="torch.html#torch.conj">conj() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.conj">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.init.html#torch.nn.init.constant_">constant_() (in module torch.nn.init)</a>
</li>
      <li><a href="nn.html#torch.nn.ConstantPad1d">ConstantPad1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ConstantPad2d">ConstantPad2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ConstantPad3d">ConstantPad3d (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.Constraint">Constraint (class in torch.distributions.constraints)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraint_registry.ConstraintRegistry">ConstraintRegistry (class in torch.distributions.constraint_registry)</a>
</li>
      <li><a href="rpc.html#torch.distributed.autograd.context">context (class in torch.distributed.autograd)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.contiguous">contiguous() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Conv1d">Conv1d (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.conv1d">conv1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Conv2d">Conv2d (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.qat.Conv2d">(class in torch.nn.qat)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.Conv2d">(class in torch.nn.quantized)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.conv2d">conv2d() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.conv2d">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Conv3d">Conv3d (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.Conv3d">(class in torch.nn.quantized)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.conv3d">conv3d() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.conv3d">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.conv_transpose1d">conv_transpose1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.conv_transpose2d">conv_transpose2d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.conv_transpose3d">conv_transpose3d() (in module torch.nn.functional)</a>
</li>
      <li><a href="quantization.html#torch.nn.intrinsic.ConvBn2d">ConvBn2d (class in torch.nn.intrinsic)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.intrinsic.qat.ConvBn2d">(class in torch.nn.intrinsic.qat)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.nn.intrinsic.ConvBnReLU2d">ConvBnReLU2d (class in torch.nn.intrinsic)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.intrinsic.qat.ConvBnReLU2d">(class in torch.nn.intrinsic.qat)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.quantization.convert">convert() (in module torch.quantization)</a>
</li>
      <li><a href="nn.html#torch.nn.SyncBatchNorm.convert_sync_batchnorm">convert_sync_batchnorm() (torch.nn.SyncBatchNorm class method)</a>
</li>
      <li><a href="quantization.html#torch.nn.intrinsic.ConvReLU2d">ConvReLU2d (class in torch.nn.intrinsic)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.intrinsic.qat.ConvReLU2d">(class in torch.nn.intrinsic.qat)</a>
</li>
        <li><a href="quantization.html#torch.nn.intrinsic.quantized.ConvReLU2d">(class in torch.nn.intrinsic.quantized)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.nn.intrinsic.ConvReLU3d">ConvReLU3d (class in torch.nn.intrinsic)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.intrinsic.quantized.ConvReLU3d">(class in torch.nn.intrinsic.quantized)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.ConvTranspose1d">ConvTranspose1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ConvTranspose2d">ConvTranspose2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ConvTranspose3d">ConvTranspose3d (class in torch.nn)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.copy_">copy_() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.copy_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.cos">cos() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cos">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.cos_">cos_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.cosh">cosh() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cosh">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.cosh_">cosh_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.cosine_embedding_loss">cosine_embedding_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.cosine_similarity">cosine_similarity() (in module torch.nn.functional)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.CosineAnnealingLR">CosineAnnealingLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts">CosineAnnealingWarmRestarts (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="nn.html#torch.nn.CosineEmbeddingLoss">CosineEmbeddingLoss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.CosineSimilarity">CosineSimilarity (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.covariance_matrix">covariance_matrix (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.covariance_matrix">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
      </ul></li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.CppExtension">CppExtension() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.cpu">cpu() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.cpu">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.cpu">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.crop">crop() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torch.html#torch.cross">cross() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cross">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.cross_entropy">cross_entropy() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.CrossEntropyLoss">CrossEntropyLoss (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.ctc_loss">ctc_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.CTCLoss">CTCLoss (class in torch.nn)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.cuda">cuda() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.cuda">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.cuda">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.CUDAExtension">CUDAExtension() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="torch.html#torch.cumprod">cumprod() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cumprod">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.cumsum">cumsum() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.cumsum">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.current_blas_handle">current_blas_handle() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.current_device">current_device() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.current_stream">current_stream() (in module torch.cuda)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.custom_from_mask">custom_from_mask() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.CustomFromMask">CustomFromMask (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.CyclicLR">CyclicLR (class in torch.optim.lr_scheduler)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="D">D</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.functional.html#torch.nn.parallel.data_parallel">data_parallel() (in module torch.nn.parallel)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.data_ptr">data_ptr() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.data_ptr">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="data.html#torch.utils.data.DataLoader">DataLoader (class in torch.utils.data)</a>
</li>
      <li><a href="nn.html#torch.nn.DataParallel">DataParallel (class in torch.nn)</a>
</li>
      <li><a href="data.html#torch.utils.data.Dataset">Dataset (class in torch.utils.data)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.DatasetFolder">DatasetFolder (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.segmentation.deeplabv3_resnet101">deeplabv3_resnet101() (in module torchvision.models.segmentation)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.segmentation.deeplabv3_resnet50">deeplabv3_resnet50() (in module torchvision.models.segmentation)</a>
</li>
      <li><a href="quantization.html#torch.quantization.default_eval_fn">default_eval_fn() (in module torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.torch.default_generator">default_generator (torch.torch attribute)</a>
</li>
      <li><a href="cuda.html#torch.cuda.default_stream">default_stream() (in module torch.cuda)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.dense_dim">dense_dim() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.densenet121">densenet121() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.densenet161">densenet161() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.densenet169">densenet169() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.densenet201">densenet201() (in module torchvision.models)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.dependent_property">dependent_property (in module torch.distributions.constraints)</a>
</li>
      <li><a href="quantization.html#torch.nn.quantized.DeQuantize">DeQuantize (class in torch.nn.quantized)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.dequantize">dequantize() (torch.Tensor method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.DeQuantStub">DeQuantStub (class in torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.det">det() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.det">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.Tensor.detach">detach() (torch.Tensor method)</a>
</li>
      <li><a href="autograd.html#torch.Tensor.detach_">detach_() (torch.Tensor method)</a>
</li>
      <li><a href="autograd.html#torch.autograd.detect_anomaly">detect_anomaly (class in torch.autograd)</a>
</li>
      <li><a href="cuda.html#torch.cuda.device">device (class in torch.cuda)</a>

      <ul>
        <li><a href="torch.html#torch._C.Generator.device">(torch._C.Generator attribute)</a>
</li>
        <li><a href="storage.html#torch.FloatStorage.device">(torch.FloatStorage attribute)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.device">(torch.Tensor attribute)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.device_count">device_count() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.device_of">device_of (class in torch.cuda)</a>
</li>
      <li><a href="distributions.html#torch.distributions.chi2.Chi2.df">df() (torch.distributions.chi2.Chi2 property)</a>
</li>
      <li><a href="torch.html#torch.diag">diag() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.diag">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.diag_embed">diag_embed() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.diag_embed">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.diagflat">diagflat() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.diagflat">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.diagonal">diagonal() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.diagonal">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.digamma">digamma() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.digamma">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.digamma_">digamma_() (torch.Tensor method)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.dim">dim() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.dim">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.init.html#torch.nn.init.dirac_">dirac_() (in module torch.nn.init)</a>
</li>
      <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet">Dirichlet (class in torch.distributions.dirichlet)</a>
</li>
      <li><a href="torch.html#torch.dist">dist() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.dist">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.parallel.DistributedDataParallel">DistributedDataParallel (class in torch.nn.parallel)</a>
</li>
      <li><a href="rpc.html#torch.distributed.optim.DistributedOptimizer">DistributedOptimizer (class in torch.distributed.optim)</a>
</li>
      <li><a href="data.html#torch.utils.data.distributed.DistributedSampler">DistributedSampler (class in torch.utils.data.distributed)</a>
</li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution">Distribution (class in torch.distributions.distribution)</a>
</li>
      <li><a href="torch.html#torch.div">div() (in module torch)</a>, <a href="torch.html#torch.div">[1]</a>, <a href="torch.html#torch.div">[2]</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.div">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.div">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.div_">div_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.div_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.dot">dot() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.dot">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.double">double() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.double">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.double">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="hub.html#torch.hub.download_url_to_file">download_url_to_file() (in module torch.hub)</a>
</li>
      <li><a href="torch.html#torch.quasirandom.SobolEngine.draw">draw() (torch.quasirandom.SobolEngine method)</a>
</li>
      <li><a href="nn.html#torch.nn.Dropout">Dropout (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.dropout">dropout() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Dropout2d">Dropout2d (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.dropout2d">dropout2d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Dropout3d">Dropout3d (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.dropout3d">dropout3d() (in module torch.nn.functional)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.dtype">dtype (torch.FloatStorage attribute)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.dump_patches">dump_patches (torch.nn.Module attribute)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="E">E</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.eig">eig() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.eig">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.einsum">einsum() (in module torch)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Event.elapsed_time">elapsed_time() (torch.cuda.Event method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.element_size">element_size() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.element_size">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.ELU">ELU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.elu">elu() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.elu_">elu_() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Embedding">Embedding (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.embedding">embedding() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.embedding_bag">embedding_bag() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.EmbeddingBag">EmbeddingBag (class in torch.nn)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.emit_nvtx">emit_nvtx (class in torch.autograd.profiler)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.EMNIST">EMNIST (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.empty">empty() (in module torch)</a>
</li>
      <li><a href="cuda.html#torch.cuda.empty_cache">empty_cache() (in module torch.cuda)</a>
</li>
      <li><a href="torch.html#torch.empty_like">empty_like() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.empty_strided">empty_strided() (in module torch)</a>
</li>
      <li><a href="autograd.html#torch.autograd.enable_grad">enable_grad (class in torch.autograd)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.entropy">entropy() (torch.distributions.bernoulli.Bernoulli method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.entropy">(torch.distributions.beta.Beta method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.entropy">(torch.distributions.categorical.Categorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.entropy">(torch.distributions.cauchy.Cauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.entropy">(torch.distributions.dirichlet.Dirichlet method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.entropy">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exp_family.ExponentialFamily.entropy">(torch.distributions.exp_family.ExponentialFamily method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.entropy">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.entropy">(torch.distributions.gamma.Gamma method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.entropy">(torch.distributions.geometric.Geometric method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.entropy">(torch.distributions.gumbel.Gumbel method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.entropy">(torch.distributions.half_cauchy.HalfCauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.entropy">(torch.distributions.half_normal.HalfNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.entropy">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.entropy">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.entropy">(torch.distributions.log_normal.LogNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.entropy">(torch.distributions.multivariate_normal.MultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.entropy">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.entropy">(torch.distributions.one_hot_categorical.OneHotCategorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.entropy">(torch.distributions.pareto.Pareto method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.entropy">(torch.distributions.studentT.StudentT method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.entropy">(torch.distributions.uniform.Uniform method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.entropy">(torch.distributions.weibull.Weibull method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.enumerate_support">enumerate_support() (torch.distributions.bernoulli.Bernoulli method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.enumerate_support">(torch.distributions.binomial.Binomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.enumerate_support">(torch.distributions.categorical.Categorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.enumerate_support">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.enumerate_support">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support">(torch.distributions.one_hot_categorical.OneHotCategorical method)</a>
</li>
      </ul></li>
      <li>
    environment variable

      <ul>
        <li><a href="jit.html#envvar-PYTORCH_JIT">PYTORCH_JIT</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.eq">eq() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.eq">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.eq_">eq_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.equal">equal() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.equal">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.erase">erase() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torch.html#torch.erf">erf() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.erf">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.erf_">erf_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.erfc">erfc() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.erfc">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.erfc_">erfc_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.erfinv">erfinv() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.erfinv">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.erfinv_">erfinv_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.eval">eval() (torch.nn.Module method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Event">Event (class in torch.cuda)</a>
</li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.event_shape">event_shape() (torch.distributions.distribution.Distribution property)</a>
</li>
      <li><a href="torch.html#torch.exp">exp() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.exp">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.exp_">exp_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.expand">expand() (torch.distributions.bernoulli.Bernoulli method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.expand">(torch.distributions.beta.Beta method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.expand">(torch.distributions.binomial.Binomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.expand">(torch.distributions.categorical.Categorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.expand">(torch.distributions.cauchy.Cauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.chi2.Chi2.expand">(torch.distributions.chi2.Chi2 method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.expand">(torch.distributions.dirichlet.Dirichlet method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.expand">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.expand">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.expand">(torch.distributions.fishersnedecor.FisherSnedecor method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.expand">(torch.distributions.gamma.Gamma method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.expand">(torch.distributions.geometric.Geometric method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.expand">(torch.distributions.gumbel.Gumbel method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.expand">(torch.distributions.half_cauchy.HalfCauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.expand">(torch.distributions.half_normal.HalfNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.expand">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.expand">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.expand">(torch.distributions.log_normal.LogNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.expand">(torch.distributions.multinomial.Multinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.expand">(torch.distributions.multivariate_normal.MultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.expand">(torch.distributions.negative_binomial.NegativeBinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.expand">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.expand">(torch.distributions.one_hot_categorical.OneHotCategorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.expand">(torch.distributions.pareto.Pareto method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.expand">(torch.distributions.poisson.Poisson method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.expand">(torch.distributions.studentT.StudentT method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.expand">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.expand">(torch.distributions.uniform.Uniform method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.expand">(torch.distributions.weibull.Weibull method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.expand">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.expand_as">expand_as() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.expm1">expm1() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.expm1">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.expm1_">expm1_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.exponential.Exponential">Exponential (class in torch.distributions.exponential)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.exponential_">exponential_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.exp_family.ExponentialFamily">ExponentialFamily (class in torch.distributions.exp_family)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.ExponentialLR">ExponentialLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="jit.html#torch.jit.export">export() (in module torch.jit)</a>

      <ul>
        <li><a href="onnx.html#torch.onnx.export">(in module torch.onnx)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.autograd.profiler.profile.export_chrome_trace">export_chrome_trace() (torch.autograd.profiler.profile method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.ExpTransform">ExpTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleList.extend">extend() (torch.nn.ModuleList method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterList.extend">(torch.nn.ParameterList method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Module.extra_repr">extra_repr() (torch.nn.Module method)</a>
</li>
      <li><a href="torch.html#torch.eye">eye() (in module torch)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.eye_">eye_() (in module torch.nn.init)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="F">F</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/datasets.html#torchvision.datasets.FakeData">FakeData (class in torchvision.datasets)</a>
</li>
      <li><a href="quantization.html#torch.quantization.FakeQuantize">FakeQuantize (class in torch.quantization)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.FashionMNIST">FashionMNIST (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.quasirandom.SobolEngine.fast_forward">fast_forward() (torch.quasirandom.SobolEngine method)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.detection.fasterrcnn_resnet50_fpn">fasterrcnn_resnet50_fpn() (in module torchvision.models.detection)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.segmentation.fcn_resnet101">fcn_resnet101() (in module torchvision.models.segmentation)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.segmentation.fcn_resnet50">fcn_resnet50() (in module torchvision.models.segmentation)</a>
</li>
      <li><a href="torch.html#torch.fft">fft() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.fft">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.fill_">fill_() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.fill_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.fill_diagonal_">fill_diagonal_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor">FisherSnedecor (class in torch.distributions.fishersnedecor)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.five_crop">five_crop() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.FiveCrop">FiveCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.Flatten">Flatten (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.flatten">flatten() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.flatten">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.RNNBase.flatten_parameters">flatten_parameters() (torch.nn.RNNBase method)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.Flickr30k">Flickr30k (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.Flickr8k">Flickr8k (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.flip">flip() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.flip">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.float">float() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.float">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.float">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.nn.quantized.FloatFunctional">FloatFunctional (class in torch.nn.quantized)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage">FloatStorage (class in torch)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor">FloatTensor (class in torch.sparse)</a>
</li>
      <li><a href="torch.html#torch.floor">floor() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.floor">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.floor_">floor_() (torch.Tensor method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.flush">flush() (torch.utils.tensorboard.writer.SummaryWriter method)</a>
</li>
      <li><a href="torch.html#torch.fmod">fmod() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.fmod">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.fmod_">fmod_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Fold">Fold (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.fold">fold() (in module torch.nn.functional)</a>
</li>
      <li><a href="random.html#torch.random.fork_rng">fork_rng() (in module torch.random)</a>, <a href="random.html#torch.random.fork_rng">[1]</a>
</li>
      <li><a href="autograd.html#torch.autograd.Function.forward">forward() (torch.autograd.Function static method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.forward">(torch.nn.Module method)</a>
</li>
        <li><a href="nn.html#torch.nn.MultiheadAttention.forward">(torch.nn.MultiheadAttention method)</a>
</li>
        <li><a href="nn.html#torch.nn.Transformer.forward">(torch.nn.Transformer method)</a>
</li>
        <li><a href="nn.html#torch.nn.TransformerDecoder.forward">(torch.nn.TransformerDecoder method)</a>
</li>
        <li><a href="nn.html#torch.nn.TransformerDecoderLayer.forward">(torch.nn.TransformerDecoderLayer method)</a>
</li>
        <li><a href="nn.html#torch.nn.TransformerEncoder.forward">(torch.nn.TransformerEncoder method)</a>
</li>
        <li><a href="nn.html#torch.nn.TransformerEncoderLayer.forward">(torch.nn.TransformerEncoderLayer method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.frac">frac() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.frac">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.frac_">frac_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.FractionalMaxPool2d">FractionalMaxPool2d (class in torch.nn)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.from_buffer">from_buffer() (torch.FloatStorage static method)</a>
</li>
      <li><a href="dlpack.html#torch.utils.dlpack.from_dlpack">from_dlpack() (in module torch.utils.dlpack)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.from_file">from_file() (torch.FloatStorage static method)</a>
</li>
      <li><a href="quantization.html#torch.nn.intrinsic.qat.ConvBn2d.from_float">from_float() (torch.nn.intrinsic.qat.ConvBn2d class method)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.qat.Conv2d.from_float">(torch.nn.qat.Conv2d class method)</a>
</li>
        <li><a href="quantization.html#torch.nn.qat.Linear.from_float">(torch.nn.qat.Linear class method)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.Conv2d.from_float">(torch.nn.quantized.Conv2d class method)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.Conv3d.from_float">(torch.nn.quantized.Conv3d class method)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.dynamic.Linear.from_float">(torch.nn.quantized.dynamic.Linear class method)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.Linear.from_float">(torch.nn.quantized.Linear class method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.Event.from_ipc_handle">from_ipc_handle() (torch.cuda.Event class method)</a>
</li>
      <li><a href="torch.html#torch.from_numpy">from_numpy() (in module torch)</a>
</li>
      <li><a href="nn.html#torch.nn.Embedding.from_pretrained">from_pretrained() (torch.nn.Embedding class method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.EmbeddingBag.from_pretrained">(torch.nn.EmbeddingBag class method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.full">full() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.full_like">full_like() (in module torch)</a>
</li>
      <li><a href="autograd.html#torch.autograd.Function">Function (class in torch.autograd)</a>
</li>
      <li><a href="quantization.html#torch.quantization.fuse_modules">fuse_modules() (in module torch.quantization)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="distributions.html#torch.distributions.gamma.Gamma">Gamma (class in torch.distributions.gamma)</a>
</li>
      <li><a href="torch.html#torch.gather">gather() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.comm.gather">(in module torch.cuda.comm)</a>
</li>
        <li><a href="distributed.html#torch.distributed.gather">(in module torch.distributed)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.gather">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.ge">ge() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ge">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.ge_">ge_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.GELU">GELU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.gelu">gelu() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Transformer.generate_square_subsequent_mask">generate_square_subsequent_mask() (torch.nn.Transformer method)</a>
</li>
      <li><a href="torch.html#torch._C.Generator">Generator (class in torch._C)</a>
</li>
      <li><a href="distributions.html#torch.distributions.geometric.Geometric">Geometric (class in torch.distributions.geometric)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.geometric_">geometric_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.geqrf">geqrf() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.geqrf">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.ger">ger() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ger">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="multiprocessing.html#torch.multiprocessing.get_all_sharing_strategies">get_all_sharing_strategies() (in module torch.multiprocessing)</a>
</li>
      <li><a href="distributed.html#torch.distributed.get_backend">get_backend() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.get_default_dtype">get_default_dtype() (in module torch)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.get_device">get_device() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.get_device">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.get_device_capability">get_device_capability() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.get_device_name">get_device_name() (in module torch.cuda)</a>
</li>
      <li><a href="rpc.html#torch.distributed.autograd.get_gradients">get_gradients() (in module torch.distributed.autograd)</a>
</li>
      <li><a href="torchvision/index.html#torchvision.get_image_backend">get_image_backend() (in module torchvision)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.CyclicLR.get_lr">get_lr() (torch.optim.lr_scheduler.CyclicLR method)</a>
</li>
      <li><a href="torch.html#torch.get_num_interop_threads">get_num_interop_threads() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.get_num_threads">get_num_threads() (in module torch)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="quantization.html#torch.quantization.get_observer_dict">get_observer_dict() (in module torch.quantization)</a>
</li>
      <li><a href="distributed.html#torch.distributed.get_rank">get_rank() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.get_rng_state">get_rng_state() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.get_rng_state">(in module torch.cuda)</a>
</li>
        <li><a href="random.html#torch.random.get_rng_state">(in module torch.random)</a>, <a href="random.html#torch.random.get_rng_state">[1]</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.get_rng_state_all">get_rng_state_all() (in module torch.cuda)</a>
</li>
      <li><a href="multiprocessing.html#torch.multiprocessing.get_sharing_strategy">get_sharing_strategy() (in module torch.multiprocessing)</a>
</li>
      <li><a href="torch.html#torch._C.Generator.get_state">get_state() (torch._C.Generator method)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.get_worker_info">get_worker_info() (in module torch.distributed.rpc)</a>

      <ul>
        <li><a href="data.html#torch.utils.data.get_worker_info">(in module torch.utils.data)</a>
</li>
      </ul></li>
      <li><a href="distributed.html#torch.distributed.get_world_size">get_world_size() (in module torch.distributed)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.global_unstructured">global_unstructured() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.glu">glu() (in module torch.nn.functional)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.googlenet">googlenet() (in module torchvision.models)</a>
</li>
      <li><a href="autograd.html#torch.Tensor.grad">grad (torch.Tensor attribute)</a>
</li>
      <li><a href="autograd.html#torch.autograd.grad">grad() (in module torch.autograd)</a>
</li>
      <li><a href="autograd.html#torch.autograd.gradcheck">gradcheck() (in module torch.autograd)</a>
</li>
      <li><a href="autograd.html#torch.autograd.gradgradcheck">gradgradcheck() (in module torch.autograd)</a>
</li>
      <li><a href="jit.html#torch.jit.ScriptModule.graph">graph() (torch.jit.ScriptModule property)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Grayscale">Grayscale (class in torchvision.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.greater_than">greater_than (in module torch.distributions.constraints)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.greater_than_eq">greater_than_eq (in module torch.distributions.constraints)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.grid_sample">grid_sample() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.GroupNorm">GroupNorm (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.GRU">GRU (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.GRUCell">GRUCell (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.gt">gt() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.gt">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.gt_">gt_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.gumbel.Gumbel">Gumbel (class in torch.distributions.gumbel)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.gumbel_softmax">gumbel_softmax() (in module torch.nn.functional)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="H">H</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="storage.html#torch.FloatStorage.half">half() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.half">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.half">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.constraints.half_open_interval">half_open_interval (in module torch.distributions.constraints)</a>
</li>
      <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy">HalfCauchy (class in torch.distributions.half_cauchy)</a>
</li>
      <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal">HalfNormal (class in torch.distributions.half_normal)</a>
</li>
      <li><a href="torch.html#torch.hamming_window">hamming_window() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.hann_window">hann_window() (in module torch)</a>
</li>
      <li><a href="nn.html#torch.nn.Hardshrink">Hardshrink (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.hardshrink">hardshrink() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.hardshrink">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Hardtanh">Hardtanh (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.hardtanh">hardtanh() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.hardtanh_">hardtanh_() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.has_enumerate_support">has_enumerate_support (torch.distributions.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.has_enumerate_support">(torch.distributions.binomial.Binomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.has_enumerate_support">(torch.distributions.categorical.Categorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.has_enumerate_support">(torch.distributions.one_hot_categorical.OneHotCategorical attribute)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.independent.Independent.has_enumerate_support">has_enumerate_support() (torch.distributions.independent.Independent property)</a>
</li>
      <li><a href="distributions.html#torch.distributions.beta.Beta.has_rsample">has_rsample (torch.distributions.beta.Beta attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.has_rsample">(torch.distributions.cauchy.Cauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.has_rsample">(torch.distributions.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.has_rsample">(torch.distributions.exponential.Exponential attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.has_rsample">(torch.distributions.fishersnedecor.FisherSnedecor attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.has_rsample">(torch.distributions.gamma.Gamma attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.has_rsample">(torch.distributions.half_cauchy.HalfCauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.has_rsample">(torch.distributions.half_normal.HalfNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.has_rsample">(torch.distributions.laplace.Laplace attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.has_rsample">(torch.distributions.log_normal.LogNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.has_rsample">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.has_rsample">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.has_rsample">(torch.distributions.normal.Normal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.has_rsample">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.has_rsample">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.has_rsample">(torch.distributions.studentT.StudentT attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.has_rsample">(torch.distributions.uniform.Uniform attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="distributions.html#torch.distributions.independent.Independent.has_rsample">has_rsample() (torch.distributions.independent.Independent property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.has_rsample">(torch.distributions.transformed_distribution.TransformedDistribution property)</a>
</li>
      </ul></li>
      <li><a href="hub.html#torch.hub.help">help() (in module torch.hub)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.hflip">hflip() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.hinge_embedding_loss">hinge_embedding_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.HingeEmbeddingLoss">HingeEmbeddingLoss (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.histc">histc() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.histc">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.quantization.HistogramObserver">HistogramObserver (class in torch.quantization)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.HMDB51">HMDB51 (class in torchvision.datasets)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.hspmm">hspmm() (torch.sparse.FloatTensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="I">I</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.icdf">icdf() (torch.distributions.cauchy.Cauchy method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.icdf">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.icdf">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.icdf">(torch.distributions.half_cauchy.HalfCauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.icdf">(torch.distributions.half_normal.HalfNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.icdf">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.icdf">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.icdf">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.icdf">(torch.distributions.uniform.Uniform method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Identity">Identity (class in torch.nn)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.Identity">(class in torch.nn.utils.prune)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.utils.prune.identity">identity() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="torch.html#torch.ifft">ifft() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ifft">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="jit.html#torch.jit.ignore">ignore() (in module torch.jit)</a>
</li>
      <li><a href="torch.html#torch.imag">imag() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.imag">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.ImageFolder">ImageFolder (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.ImageNet">ImageNet (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.inception_v3">inception_v3() (in module torchvision.models)</a>
</li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.include_paths">include_paths() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="distributions.html#torch.distributions.independent.Independent">Independent (class in torch.distributions.independent)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_add">index_add() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_add_">index_add_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_copy">index_copy() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_copy_">index_copy_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_fill">index_fill() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_fill_">index_fill_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_put">index_put() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.index_put_">index_put_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.index_select">index_select() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.index_select">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.indices">indices() (torch.Tensor method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.init">init() (in module torch.cuda)</a>
</li>
      <li><a href="distributed.html#torch.distributed.init_process_group">init_process_group() (in module torch.distributed)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.init_rpc">init_rpc() (in module torch.distributed.rpc)</a>
</li>
      <li><a href="torch.html#torch.initial_seed">initial_seed() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.initial_seed">(in module torch.cuda)</a>
</li>
        <li><a href="random.html#torch.random.initial_seed">(in module torch.random)</a>, <a href="random.html#torch.random.initial_seed">[1]</a>
</li>
        <li><a href="torch.html#torch._C.Generator.initial_seed">(torch._C.Generator method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.ModuleList.insert">insert() (torch.nn.ModuleList method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.instance_norm">instance_norm() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.InstanceNorm1d">InstanceNorm1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.InstanceNorm2d">InstanceNorm2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.InstanceNorm3d">InstanceNorm3d (class in torch.nn)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.int">int() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.int">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.int_repr">int_repr() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.integer_interval">integer_interval (in module torch.distributions.constraints)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.interpolate">interpolate() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.interpolate">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.constraints.interval">interval (in module torch.distributions.constraints)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.Transform.inv">inv() (torch.distributions.transforms.Transform property)</a>
</li>
      <li><a href="torch.html#torch.inverse">inverse() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.inverse">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.ipc_collect">ipc_collect() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Event.ipc_handle">ipc_handle() (torch.cuda.Event method)</a>
</li>
      <li><a href="distributed.html#torch.distributed.irecv">irecv() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.irfft">irfft() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.irfft">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.is_available">is_available() (in module torch.cuda)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.is_coalesced">is_coalesced() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.is_contiguous">is_contiguous() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.is_cuda">is_cuda (torch.FloatStorage attribute)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.is_cuda">(torch.Tensor attribute)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.is_floating_point">is_floating_point() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.is_floating_point">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="onnx.html#torch.onnx.is_in_onnx_export">is_in_onnx_export() (in module torch.onnx)</a>
</li>
      <li><a href="cuda.html#torch.cuda.is_initialized">is_initialized() (in module torch.cuda)</a>

      <ul>
        <li><a href="distributed.html#torch.distributed.is_initialized">(in module torch.distributed)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.Tensor.is_leaf">is_leaf (torch.Tensor attribute)</a>
</li>
      <li><a href="distributed.html#torch.distributed.is_mpi_available">is_mpi_available() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.is_nccl_available">is_nccl_available() (in module torch.distributed)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.RRef.is_owner">is_owner() (torch.distributed.rpc.RRef method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.is_pinned">is_pinned() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.is_pinned">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.utils.prune.is_pruned">is_pruned() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="jit.html#torch.jit.is_scripting">is_scripting() (in module torch.jit)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.is_set_to">is_set_to() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.is_shared">is_shared() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.is_shared">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.is_signed">is_signed() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.is_sparse">is_sparse (torch.FloatStorage attribute)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.is_sparse">(torch.Tensor attribute)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.is_storage">is_storage() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.is_tensor">is_tensor() (in module torch)</a>
</li>
      <li><a href="distributed.html#torch.distributed.isend">isend() (in module torch.distributed)</a>
</li>
      <li><a href="torch.html#torch.isfinite">isfinite() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.isinf">isinf() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.isnan">isnan() (in module torch)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.item">item() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict.items">items() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.items">(torch.nn.ParameterDict method)</a>
</li>
      </ul></li>
      <li><a href="data.html#torch.utils.data.IterableDataset">IterableDataset (class in torch.utils.data)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="J">J</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="multiprocessing.html#torch.multiprocessing.SpawnContext.join">join() (torch.multiprocessing.SpawnContext method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="K">K</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.init.html#torch.nn.init.kaiming_normal_">kaiming_normal_() (in module torch.nn.init)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.kaiming_uniform_">kaiming_uniform_() (in module torch.nn.init)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.profile.key_averages">key_averages() (torch.autograd.profiler.profile method)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.detection.keypointrcnn_resnet50_fpn">keypointrcnn_resnet50_fpn() (in module torchvision.models.detection)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict.keys">keys() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.keys">(torch.nn.ParameterDict method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/datasets.html#torchvision.datasets.Kinetics400">Kinetics400 (class in torchvision.datasets)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.kl_div">kl_div() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.kl.kl_divergence">kl_divergence() (in module torch.distributions.kl)</a>
</li>
      <li><a href="nn.html#torch.nn.KLDivLoss">KLDivLoss (class in torch.nn)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.KMNIST">KMNIST (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.kthvalue">kthvalue() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.kthvalue">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="L">L</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.functional.html#torch.nn.functional.l1_loss">l1_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.l1_unstructured">l1_unstructured() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.html#torch.nn.L1Loss">L1Loss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.L1Unstructured">L1Unstructured (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Lambda">Lambda (class in torchvision.transforms)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.LambdaLR">LambdaLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="distributions.html#torch.distributions.laplace.Laplace">Laplace (class in torch.distributions.laplace)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.layer_norm">layer_norm() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.LayerNorm">LayerNorm (class in torch.nn)</a>
</li>
      <li><a href="optim.html#torch.optim.LBFGS">LBFGS (class in torch.optim)</a>
</li>
      <li><a href="torch.html#torch.le">le() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.le">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.le_">le_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.leaky_relu">leaky_relu() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.leaky_relu_">leaky_relu_() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.LeakyReLU">LeakyReLU (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.lerp">lerp() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lerp">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.lerp_">lerp_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.less_than">less_than (in module torch.distributions.constraints)</a>
</li>
      <li><a href="torch.html#torch.lgamma">lgamma() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lgamma">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.lgamma_">lgamma_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Linear">Linear (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.qat.Linear">(class in torch.nn.qat)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.Linear">(class in torch.nn.quantized)</a>
</li>
        <li><a href="quantization.html#torch.nn.quantized.dynamic.Linear">(class in torch.nn.quantized.dynamic)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.linear">linear() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.linear">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.nn.intrinsic.LinearReLU">LinearReLU (class in torch.nn.intrinsic)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.intrinsic.qat.LinearReLU">(class in torch.nn.intrinsic.qat)</a>
</li>
        <li><a href="quantization.html#torch.nn.intrinsic.quantized.LinearReLU">(class in torch.nn.intrinsic.quantized)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.LinearTransformation">LinearTransformation (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.linspace">linspace() (in module torch)</a>
</li>
      <li><a href="hub.html#torch.hub.list">list() (in module torch.hub)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.ln_structured">ln_structured() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.LnStructured">LnStructured (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="torch.html#torch.load">load() (in module torch)</a>

      <ul>
        <li><a href="hub.html#torch.hub.load">(in module torch.hub)</a>
</li>
        <li><a href="jit.html#torch.jit.load">(in module torch.jit)</a>
</li>
        <li><a href="cpp_extension.html#torch.utils.cpp_extension.load">(in module torch.utils.cpp_extension)</a>
</li>
      </ul></li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.load_inline">load_inline() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.load_nvprof">load_nvprof() (in module torch.autograd.profiler)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.load_state_dict">load_state_dict() (torch.nn.Module method)</a>

      <ul>
        <li><a href="optim.html#torch.optim.lr_scheduler.LambdaLR.load_state_dict">(torch.optim.lr_scheduler.LambdaLR method)</a>
</li>
        <li><a href="optim.html#torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict">(torch.optim.lr_scheduler.MultiplicativeLR method)</a>
</li>
        <li><a href="optim.html#torch.optim.Optimizer.load_state_dict">(torch.optim.Optimizer method)</a>
</li>
      </ul></li>
      <li><a href="hub.html#torch.hub.load_state_dict_from_url">load_state_dict_from_url() (in module torch.hub)</a>
</li>
      <li><a href="model_zoo.html#torch.utils.model_zoo.load_url">load_url() (in module torch.utils.model_zoo)</a>
</li>
      <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.loc">loc() (torch.distributions.log_normal.LogNormal property)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.local_response_norm">local_response_norm() (in module torch.nn.functional)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.RRef.local_value">local_value() (torch.distributed.rpc.RRef method)</a>
</li>
      <li><a href="nn.html#torch.nn.LocalResponseNorm">LocalResponseNorm (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.log">log() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.log">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.log10">log10() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.log10">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.log10_">log10_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.log1p">log1p() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.log1p">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.log1p_">log1p_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.log2">log2() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.log2">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.log2_">log2_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.log_">log_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.Transform.log_abs_det_jacobian">log_abs_det_jacobian() (torch.distributions.transforms.Transform method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.log_normal_">log_normal_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.log_prob">log_prob() (torch.distributions.bernoulli.Bernoulli method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.log_prob">(torch.distributions.beta.Beta method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.log_prob">(torch.distributions.binomial.Binomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.log_prob">(torch.distributions.categorical.Categorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.log_prob">(torch.distributions.cauchy.Cauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.log_prob">(torch.distributions.dirichlet.Dirichlet method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.log_prob">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.log_prob">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.log_prob">(torch.distributions.fishersnedecor.FisherSnedecor method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.log_prob">(torch.distributions.gamma.Gamma method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.log_prob">(torch.distributions.geometric.Geometric method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.log_prob">(torch.distributions.gumbel.Gumbel method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.log_prob">(torch.distributions.half_cauchy.HalfCauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.log_prob">(torch.distributions.half_normal.HalfNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.log_prob">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.log_prob">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.log_prob">(torch.distributions.multinomial.Multinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.log_prob">(torch.distributions.multivariate_normal.MultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.log_prob">(torch.distributions.negative_binomial.NegativeBinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.log_prob">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob">(torch.distributions.one_hot_categorical.OneHotCategorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.log_prob">(torch.distributions.poisson.Poisson method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.log_prob">(torch.distributions.studentT.StudentT method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.log_prob">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.log_prob">(torch.distributions.uniform.Uniform method)</a>
</li>
        <li><a href="nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob">(torch.nn.AdaptiveLogSoftmaxWithLoss method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.functional.html#torch.nn.functional.log_softmax">log_softmax() (in module torch.nn.functional)</a>
</li>
      <li><a href="torch.html#torch.logdet">logdet() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.logdet">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.logical_not">logical_not() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.logical_not">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.logical_not_">logical_not_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.logical_xor">logical_xor() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.logical_xor">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.logical_xor_">logical_xor_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli">LogitRelaxedBernoulli (class in torch.distributions.relaxed_bernoulli)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.logits">logits (torch.distributions.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.logits">(torch.distributions.binomial.Binomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.logits">(torch.distributions.categorical.Categorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.logits">(torch.distributions.geometric.Geometric attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.logits">(torch.distributions.negative_binomial.NegativeBinomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli attribute)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.logits">logits() (torch.distributions.multinomial.Multinomial property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.logits">(torch.distributions.one_hot_categorical.OneHotCategorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.logits">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.logits">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical property)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.log_normal.LogNormal">LogNormal (class in torch.distributions.log_normal)</a>
</li>
      <li><a href="nn.html#torch.nn.LogSigmoid">LogSigmoid (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.logsigmoid">logsigmoid() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.LogSoftmax">LogSoftmax (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.logspace">logspace() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.logsumexp">logsumexp() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.logsumexp">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.long">long() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.long">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.transforms.LowerCholeskyTransform">LowerCholeskyTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal">LowRankMultivariateNormal (class in torch.distributions.lowrank_multivariate_normal)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.lp_pool1d">lp_pool1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.lp_pool2d">lp_pool2d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.LPPool1d">LPPool1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.LPPool2d">LPPool2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.LSTM">LSTM (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.dynamic.LSTM">(class in torch.nn.quantized.dynamic)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.LSTMCell">LSTMCell (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.lstsq">lstsq() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lstsq">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.LSUN">LSUN (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.lt">lt() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lt">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.lt_">lt_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.lu">lu() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lu">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.lu_solve">lu_solve() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.lu_solve">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.lu_unpack">lu_unpack() (in module torch)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/utils.html#torchvision.utils.make_grid">make_grid() (in module torchvision.utils)</a>
</li>
      <li><a href="torch.html#torch.manual_seed">manual_seed() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.manual_seed">(in module torch.cuda)</a>
</li>
        <li><a href="random.html#torch.random.manual_seed">(in module torch.random)</a>, <a href="random.html#torch.random.manual_seed">[1]</a>
</li>
        <li><a href="torch.html#torch._C.Generator.manual_seed">(torch._C.Generator method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.manual_seed_all">manual_seed_all() (in module torch.cuda)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.map_">map_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.margin_ranking_loss">margin_ranking_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.MarginRankingLoss">MarginRankingLoss (class in torch.nn)</a>
</li>
      <li><a href="cuda.html#torch.cuda.nvtx.mark">mark() (in module torch.cuda.nvtx)</a>
</li>
      <li><a href="autograd.html#torch.autograd.function._ContextMethodMixin.mark_dirty">mark_dirty() (torch.autograd.function._ContextMethodMixin method)</a>
</li>
      <li><a href="autograd.html#torch.autograd.function._ContextMethodMixin.mark_non_differentiable">mark_non_differentiable() (torch.autograd.function._ContextMethodMixin method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.masked_fill">masked_fill() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.masked_fill_">masked_fill_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.masked_scatter">masked_scatter() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.masked_scatter_">masked_scatter_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.masked_select">masked_select() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.masked_select">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/models.html#torchvision.models.detection.maskrcnn_resnet50_fpn">maskrcnn_resnet50_fpn() (in module torchvision.models.detection)</a>
</li>
      <li><a href="torch.html#torch.matmul">matmul() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.matmul">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.matrix_power">matrix_power() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.matrix_power">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.matrix_rank">matrix_rank() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.max">max() (in module torch)</a>, <a href="torch.html#torch.max">[1]</a>, <a href="torch.html#torch.max">[2]</a>, <a href="torch.html#torch.max">[3]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.max">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.max_memory_allocated">max_memory_allocated() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.max_memory_cached">max_memory_cached() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.max_memory_reserved">max_memory_reserved() (in module torch.cuda)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.max_pool1d">max_pool1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.max_pool2d">max_pool2d() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.max_pool2d">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.max_pool3d">max_pool3d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.max_unpool1d">max_unpool1d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.max_unpool2d">max_unpool2d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.max_unpool3d">max_unpool3d() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxPool1d">MaxPool1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxPool2d">MaxPool2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxPool3d">MaxPool3d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxUnpool1d">MaxUnpool1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxUnpool2d">MaxUnpool2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MaxUnpool3d">MaxUnpool3d (class in torch.nn)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.video.mc3_18">mc3_18() (in module torchvision.models.video)</a>
</li>
      <li><a href="torch.html#torch.mean">mean() (in module torch)</a>, <a href="torch.html#torch.mean">[1]</a>, <a href="torch.html#torch.mean">[2]</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.mean">(torch.distributions.bernoulli.Bernoulli property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.beta.Beta.mean">(torch.distributions.beta.Beta property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.mean">(torch.distributions.binomial.Binomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.mean">(torch.distributions.categorical.Categorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.mean">(torch.distributions.cauchy.Cauchy property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.mean">(torch.distributions.dirichlet.Dirichlet property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.mean">(torch.distributions.distribution.Distribution property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.mean">(torch.distributions.exponential.Exponential property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.mean">(torch.distributions.fishersnedecor.FisherSnedecor property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.mean">(torch.distributions.gamma.Gamma property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.mean">(torch.distributions.geometric.Geometric property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.mean">(torch.distributions.gumbel.Gumbel property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.mean">(torch.distributions.half_cauchy.HalfCauchy property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.mean">(torch.distributions.half_normal.HalfNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.mean">(torch.distributions.independent.Independent property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.mean">(torch.distributions.laplace.Laplace property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.mean">(torch.distributions.log_normal.LogNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.mean">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.mean">(torch.distributions.multinomial.Multinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.mean">(torch.distributions.multivariate_normal.MultivariateNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.mean">(torch.distributions.negative_binomial.NegativeBinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.mean">(torch.distributions.normal.Normal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.mean">(torch.distributions.one_hot_categorical.OneHotCategorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.mean">(torch.distributions.pareto.Pareto property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.mean">(torch.distributions.poisson.Poisson property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.mean">(torch.distributions.studentT.StudentT property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.mean">(torch.distributions.uniform.Uniform property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.mean">(torch.distributions.weibull.Weibull property)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.mean">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.median">median() (in module torch)</a>, <a href="torch.html#torch.median">[1]</a>, <a href="torch.html#torch.median">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.median">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.memory_allocated">memory_allocated() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.memory_cached">memory_cached() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.memory_reserved">memory_reserved() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.memory_snapshot">memory_snapshot() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.memory_stats">memory_stats() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.memory_summary">memory_summary() (in module torch.cuda)</a>
</li>
      <li><a href="torch.html#torch.meshgrid">meshgrid() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.min">min() (in module torch)</a>, <a href="torch.html#torch.min">[1]</a>, <a href="torch.html#torch.min">[2]</a>, <a href="torch.html#torch.min">[3]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.min">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.quantization.MinMaxObserver">MinMaxObserver (class in torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.mm">mm() (in module torch)</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.mm">(in module torch.sparse)</a>
</li>
        <li><a href="sparse.html#torch.sparse.FloatTensor.mm">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.mm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/models.html#torchvision.models.mnasnet0_5">mnasnet0_5() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.mnasnet0_75">mnasnet0_75() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.mnasnet1_0">mnasnet1_0() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.mnasnet1_3">mnasnet1_3() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.MNIST">MNIST (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.mobilenet_v2">mobilenet_v2() (in module torchvision.models)</a>
</li>
      <li><a href="torch.html#torch.mode">mode() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.mode">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Module">Module (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.to">Module.to() (in module torch.nn)</a>, <a href="nn.html#torch.nn.Module.to">[1]</a>, <a href="nn.html#torch.nn.Module.to">[2]</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict">ModuleDict (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleList">ModuleList (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.modules">modules() (torch.nn.Module method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.MovingAverageMinMaxObserver">MovingAverageMinMaxObserver (class in torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.MovingAveragePerChannelMinMaxObserver">MovingAveragePerChannelMinMaxObserver (class in torch.quantization)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.mse_loss">mse_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.MSELoss">MSELoss (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.mul">mul() (in module torch)</a>, <a href="torch.html#torch.mul">[1]</a>, <a href="torch.html#torch.mul">[2]</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.mul">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.mul">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.mul_">mul_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.mul_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.multi_margin_loss">multi_margin_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.MultiheadAttention">MultiheadAttention (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.multilabel_margin_loss">multilabel_margin_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.multilabel_soft_margin_loss">multilabel_soft_margin_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.MultiLabelMarginLoss">MultiLabelMarginLoss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MultiLabelSoftMarginLoss">MultiLabelSoftMarginLoss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.MultiMarginLoss">MultiMarginLoss (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.multinomial.Multinomial">Multinomial (class in torch.distributions.multinomial)</a>
</li>
      <li><a href="torch.html#torch.multinomial">multinomial() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.multinomial">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="optim.html#torch.optim.lr_scheduler.MultiplicativeLR">MultiplicativeLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.MultiStepLR">MultiStepLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal">MultivariateNormal (class in torch.distributions.multivariate_normal)</a>
</li>
      <li><a href="torch.html#torch.mv">mv() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.mv">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.mvlgamma">mvlgamma() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.mvlgamma">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.mvlgamma_">mvlgamma_() (torch.Tensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="N">N</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.Module.named_buffers">named_buffers() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.named_children">named_children() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.named_modules">named_modules() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.named_parameters">named_parameters() (torch.nn.Module method)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.names">names (torch.Tensor attribute)</a>
</li>
      <li><a href="torch.html#torch.narrow">narrow() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.narrow">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.narrow_copy">narrow_copy() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.narrow_copy">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.ndim">ndim (torch.Tensor attribute)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.ndimension">ndimension() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.ne">ne() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ne">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.ne_">ne_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.neg">neg() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.neg">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.neg_">neg_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial">NegativeBinomial (class in torch.distributions.negative_binomial)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.nelement">nelement() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.new">new() (torch.FloatStorage method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.new_empty">new_empty() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.new_full">new_full() (torch.Tensor method)</a>
</li>
      <li><a href="distributed.html#torch.distributed.new_group">new_group() (in module torch.distributed)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.new_ones">new_ones() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.new_tensor">new_tensor() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.new_zeros">new_zeros() (torch.Tensor method)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.nll_loss">nll_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.NLLLoss">NLLLoss (class in torch.nn)</a>
</li>
      <li><a href="torchvision/ops.html#torchvision.ops.nms">nms() (in module torchvision.ops)</a>
</li>
      <li><a href="autograd.html#torch.autograd.no_grad">no_grad (class in torch.autograd)</a>
</li>
      <li><a href="nn.html#torch.nn.parallel.DistributedDataParallel.no_sync">no_sync() (torch.nn.parallel.DistributedDataParallel method)</a>
</li>
      <li><a href="torch.html#torch.nonzero">nonzero() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.nonzero">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="quantization.html#torch.quantization.NoopObserver">NoopObserver (class in torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.norm">norm() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.norm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.normal.Normal">Normal (class in torch.distributions.normal)</a>
</li>
      <li><a href="torch.html#torch.normal">normal() (in module torch)</a>, <a href="torch.html#torch.normal">[1]</a>, <a href="torch.html#torch.normal">[2]</a>, <a href="torch.html#torch.normal">[3]</a>, <a href="torch.html#torch.normal">[4]</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.normal_">normal_() (in module torch.nn.init)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.normal_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Normalize">Normalize (class in torchvision.transforms)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.normalize">normalize() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="torchvision/transforms.html#torchvision.transforms.functional.normalize">(in module torchvision.transforms.functional)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.numel">numel() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.numel">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.numpy">numpy() (torch.Tensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="O">O</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.functional.html#torch.nn.functional.one_hot">one_hot() (in module torch.nn.functional)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.OneCycleLR">OneCycleLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical">OneHotCategorical (class in torch.distributions.one_hot_categorical)</a>
</li>
      <li><a href="torch.html#torch.ones">ones() (in module torch)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.ones_">ones_() (in module torch.nn.init)</a>
</li>
      <li><a href="torch.html#torch.ones_like">ones_like() (in module torch)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="optim.html#torch.optim.Optimizer">Optimizer (class in torch.optim)</a>
</li>
      <li><a href="torch.html#torch.orgqr">orgqr() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.orgqr">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.ormqr">ormqr() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.ormqr">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.init.html#torch.nn.init.orthogonal_">orthogonal_() (in module torch.nn.init)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.RRef.owner">owner() (torch.distributed.rpc.RRef method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.utils.rnn.pack_padded_sequence">pack_padded_sequence() (in module torch.nn.utils.rnn)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.rnn.pack_sequence">pack_sequence() (in module torch.nn.utils.rnn)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.rnn.PackedSequence">PackedSequence() (in module torch.nn.utils.rnn)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Pad">Pad (class in torchvision.transforms)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.pad">pad() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="torchvision/transforms.html#torchvision.transforms.functional.pad">(in module torchvision.transforms.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.utils.rnn.pad_packed_sequence">pad_packed_sequence() (in module torch.nn.utils.rnn)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.rnn.pad_sequence">pad_sequence() (in module torch.nn.utils.rnn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.pairwise_distance">pairwise_distance() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.PairwiseDistance">PairwiseDistance (class in torch.nn)</a>
</li>
      <li><a href="__config__.html#torch.__config__.parallel_info">parallel_info() (in module torch.__config__)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.param_shape">param_shape() (torch.distributions.bernoulli.Bernoulli property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.param_shape">(torch.distributions.binomial.Binomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.param_shape">(torch.distributions.categorical.Categorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.param_shape">(torch.distributions.multinomial.Multinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.param_shape">(torch.distributions.negative_binomial.NegativeBinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.param_shape">(torch.distributions.one_hot_categorical.OneHotCategorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.param_shape">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli property)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Parameter">Parameter (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ParameterDict">ParameterDict (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ParameterList">ParameterList (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.parameters">parameters() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.parameters_to_vector">parameters_to_vector() (in module torch.nn.utils)</a>
</li>
      <li><a href="distributions.html#torch.distributions.pareto.Pareto">Pareto (class in torch.distributions.pareto)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.pdist">pdist() (in module torch.nn.functional)</a>
</li>
      <li><a href="quantization.html#torch.quantization.PerChannelMinMaxObserver">PerChannelMinMaxObserver (class in torch.quantization)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.permute">permute() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.perplexity">perplexity() (torch.distributions.distribution.Distribution method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.perspective">perspective() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.PhotoTour">PhotoTour (class in torchvision.datasets)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.pin_memory">pin_memory() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.pin_memory">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.pinverse">pinverse() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.pinverse">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.pixel_shuffle">pixel_shuffle() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.PixelShuffle">PixelShuffle (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.poisson.Poisson">Poisson (class in torch.distributions.poisson)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.poisson_nll_loss">poisson_nll_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.PoissonNLLLoss">PoissonNLLLoss (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.polygamma">polygamma() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.polygamma">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.polygamma_">polygamma_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict.pop">pop() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.pop">(torch.nn.ParameterDict method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.pow">pow() (in module torch)</a>, <a href="torch.html#torch.pow">[1]</a>, <a href="torch.html#torch.pow">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.pow">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.pow_">pow_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.PowerTransform">PowerTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.precision_matrix">precision_matrix (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.precision_matrix">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss.predict">predict() (torch.nn.AdaptiveLogSoftmaxWithLoss method)</a>
</li>
      <li><a href="nn.html#torch.nn.PReLU">PReLU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.prelu">prelu() (in module torch.nn.functional)</a>
</li>
      <li><a href="quantization.html#torch.quantization.prepare">prepare() (in module torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.prepare_qat">prepare_qat() (in module torch.quantization)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.probs">probs (torch.distributions.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.probs">(torch.distributions.binomial.Binomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.probs">(torch.distributions.categorical.Categorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.probs">(torch.distributions.geometric.Geometric attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.probs">(torch.distributions.negative_binomial.NegativeBinomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.probs">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli attribute)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.probs">probs() (torch.distributions.multinomial.Multinomial property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.probs">(torch.distributions.one_hot_categorical.OneHotCategorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.probs">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.probs">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical property)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.prod">prod() (in module torch)</a>, <a href="torch.html#torch.prod">[1]</a>, <a href="torch.html#torch.prod">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.prod">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.autograd.profiler.profile">profile (class in torch.autograd.profiler)</a>
</li>
      <li><a href="torch.html#torch.promote_types">promote_types() (in module torch)</a>
</li>
      <li><a href="quantization.html#torch.quantization.propagate_qconfig_">propagate_qconfig_() (in module torch.quantization)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod.prune">prune() (torch.nn.utils.prune.BasePruningMethod method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.CustomFromMask.prune">(torch.nn.utils.prune.CustomFromMask method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.Identity.prune">(torch.nn.utils.prune.Identity method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.L1Unstructured.prune">(torch.nn.utils.prune.L1Unstructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.LnStructured.prune">(torch.nn.utils.prune.LnStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.prune">(torch.nn.utils.prune.PruningContainer method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomStructured.prune">(torch.nn.utils.prune.RandomStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomUnstructured.prune">(torch.nn.utils.prune.RandomUnstructured method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.utils.prune.PruningContainer">PruningContainer (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.put_">put_() (torch.Tensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="Q">Q</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.q_per_channel_axis">q_per_channel_axis() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.q_per_channel_scales">q_per_channel_scales() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.q_per_channel_zero_points">q_per_channel_zero_points() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.q_scale">q_scale() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.q_zero_point">q_zero_point() (torch.Tensor method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.QConfig">QConfig (class in torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.QConfigDynamic">QConfigDynamic (class in torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.nn.quantized.QFunctional">QFunctional (class in torch.nn.quantized)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.QMNIST">QMNIST (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.qr">qr() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.qr">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.qscheme">qscheme() (torch.Tensor method)</a>
</li>
      <li><a href="quantization.html#torch.nn.quantized.Quantize">Quantize (class in torch.nn.quantized)</a>
</li>
      <li><a href="quantization.html#torch.quantization.quantize">quantize() (in module torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.quantize_dynamic">quantize_dynamic() (in module torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.quantize_per_channel">quantize_per_channel() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.quantize_per_tensor">quantize_per_tensor() (in module torch)</a>
</li>
      <li><a href="quantization.html#torch.quantization.quantize_qat">quantize_qat() (in module torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.QuantStub">QuantStub (class in torch.quantization)</a>
</li>
      <li><a href="quantization.html#torch.quantization.QuantWrapper">QuantWrapper (class in torch.quantization)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Event.query">query() (torch.cuda.Event method)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.Stream.query">(torch.cuda.Stream method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/models.html#torchvision.models.video.r2plus1d_18">r2plus1d_18() (in module torchvision.models.video)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.video.r3d_18">r3d_18() (in module torchvision.models.video)</a>
</li>
      <li><a href="torch.html#torch.rand">rand() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.rand_like">rand_like() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.randint">randint() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.randint_like">randint_like() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.randn">randn() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.randn_like">randn_like() (in module torch)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.random_">random_() (torch.Tensor method)</a>
</li>
      <li><a href="data.html#torch.utils.data.random_split">random_split() (in module torch.utils.data)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.random_structured">random_structured() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.random_unstructured">random_unstructured() (in module torch.nn.utils.prune)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomAffine">RandomAffine (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomApply">RandomApply (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomChoice">RandomChoice (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomCrop">RandomCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomErasing">RandomErasing (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomGrayscale">RandomGrayscale (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomHorizontalFlip">RandomHorizontalFlip (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomOrder">RandomOrder (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomPerspective">RandomPerspective (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomResizedCrop">RandomResizedCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomRotation">RandomRotation (class in torchvision.transforms)</a>
</li>
      <li><a href="data.html#torch.utils.data.RandomSampler">RandomSampler (class in torch.utils.data)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomSizedCrop">RandomSizedCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.RandomStructured">RandomStructured (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.RandomUnstructured">RandomUnstructured (class in torch.nn.utils.prune)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.RandomVerticalFlip">RandomVerticalFlip (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.randperm">randperm() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.range">range() (in module torch)</a>
</li>
      <li><a href="cuda.html#torch.cuda.nvtx.range_pop">range_pop() (in module torch.cuda.nvtx)</a>
</li>
      <li><a href="cuda.html#torch.cuda.nvtx.range_push">range_push() (in module torch.cuda.nvtx)</a>
</li>
      <li><a href="torchvision/io.html#torchvision.io.read_video">read_video() (in module torchvision.io)</a>
</li>
      <li><a href="torchvision/io.html#torchvision.io.read_video_timestamps">read_video_timestamps() (in module torchvision.io)</a>
</li>
      <li><a href="torch.html#torch.real">real() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.real">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.reciprocal">reciprocal() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.reciprocal">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.reciprocal_">reciprocal_() (torch.Tensor method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Event.record">record() (torch.cuda.Event method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Stream.record_event">record_event() (torch.cuda.Stream method)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.record_function">record_function (class in torch.autograd.profiler)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.record_stream">record_stream() (torch.Tensor method)</a>
</li>
      <li><a href="quantization.html#torch.quantization.RecordingObserver">RecordingObserver (class in torch.quantization)</a>
</li>
      <li><a href="distributed.html#torch.distributed.recv">recv() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.reduce">reduce() (in module torch.distributed)</a>
</li>
      <li><a href="cuda.html#torch.cuda.comm.reduce_add">reduce_add() (in module torch.cuda.comm)</a>
</li>
      <li><a href="distributed.html#torch.distributed.reduce_multigpu">reduce_multigpu() (in module torch.distributed)</a>
</li>
      <li><a href="distributed.html#torch.distributed.reduce_op">reduce_op (class in torch.distributed)</a>
</li>
      <li><a href="optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau">ReduceLROnPlateau (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="distributed.html#torch.distributed.ReduceOp">ReduceOp (class in torch.distributed)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.refine_names">refine_names() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.ReflectionPad1d">ReflectionPad1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ReflectionPad2d">ReflectionPad2d (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraint_registry.ConstraintRegistry.register">register() (torch.distributions.constraint_registry.ConstraintRegistry method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.register_backward_hook">register_backward_hook() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.register_buffer">register_buffer() (torch.nn.Module method)</a>
</li>
      <li><a href="onnx.html#torch.onnx.register_custom_op_symbolic">register_custom_op_symbolic() (in module torch.onnx)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.register_forward_hook">register_forward_hook() (torch.nn.Module method)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.register_forward_pre_hook">register_forward_pre_hook() (torch.nn.Module method)</a>
</li>
      <li><a href="autograd.html#torch.Tensor.register_hook">register_hook() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.kl.register_kl">register_kl() (in module torch.distributions.kl)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.register_parameter">register_parameter() (torch.nn.Module method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli">RelaxedBernoulli (class in torch.distributions.relaxed_bernoulli)</a>
</li>
      <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical">RelaxedOneHotCategorical (class in torch.distributions.relaxed_categorical)</a>
</li>
      <li><a href="nn.html#torch.nn.ReLU">ReLU (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.ReLU">(class in torch.nn.quantized)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.relu">relu() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.relu">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.ReLU6">ReLU6 (class in torch.nn)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.ReLU6">(class in torch.nn.quantized)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.relu6">relu6() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.relu_">relu_() (in module torch.nn.functional)</a>
</li>
      <li><a href="torch.html#torch.remainder">remainder() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.remainder">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.remainder_">remainder_() (torch.Tensor method)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.remote">remote() (in module torch.distributed.rpc)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.prune.remove">remove() (in module torch.nn.utils.prune)</a>

      <ul>
        <li><a href="nn.html#torch.nn.utils.prune.BasePruningMethod.remove">(torch.nn.utils.prune.BasePruningMethod method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.CustomFromMask.remove">(torch.nn.utils.prune.CustomFromMask method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.Identity.remove">(torch.nn.utils.prune.Identity method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.L1Unstructured.remove">(torch.nn.utils.prune.L1Unstructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.LnStructured.remove">(torch.nn.utils.prune.LnStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.PruningContainer.remove">(torch.nn.utils.prune.PruningContainer method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomStructured.remove">(torch.nn.utils.prune.RandomStructured method)</a>
</li>
        <li><a href="nn.html#torch.nn.utils.prune.RandomUnstructured.remove">(torch.nn.utils.prune.RandomUnstructured method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.utils.remove_spectral_norm">remove_spectral_norm() (in module torch.nn.utils)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.remove_weight_norm">remove_weight_norm() (in module torch.nn.utils)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.rename">rename() (torch.Tensor method)</a>
</li>
      <li><a href="named_tensor.html#torch.Tensor.rename_">rename_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.renorm">renorm() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.renorm">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.renorm_">renorm_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.repeat">repeat() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.repeat_interleave">repeat_interleave() (in module torch)</a>, <a href="torch.html#torch.repeat_interleave">[1]</a>, <a href="torch.html#torch.repeat_interleave">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.repeat_interleave">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.ReplicationPad1d">ReplicationPad1d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ReplicationPad2d">ReplicationPad2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.ReplicationPad3d">ReplicationPad3d (class in torch.nn)</a>
</li>
      <li><a href="autograd.html#torch.Tensor.requires_grad">requires_grad (torch.Tensor attribute)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.requires_grad_">requires_grad_() (torch.nn.Module method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.requires_grad_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.quasirandom.SobolEngine.reset">reset() (torch.quasirandom.SobolEngine method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.reset_max_memory_allocated">reset_max_memory_allocated() (in module torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.reset_max_memory_cached">reset_max_memory_cached() (in module torch.cuda)</a>
</li>
      <li><a href="torch.html#torch.reshape">reshape() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.reshape">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.reshape_as">reshape_as() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Resize">Resize (class in torchvision.transforms)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.resize">resize() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.resize_">resize_() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.resize_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.resize_as_">resize_as_() (torch.Tensor method)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.resizeAs_">resizeAs_() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.resized_crop">resized_crop() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnet101">resnet101() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnet152">resnet152() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnet18">resnet18() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnet34">resnet34() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnet50">resnet50() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnext101_32x8d">resnext101_32x8d() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.resnext50_32x4d">resnext50_32x4d() (in module torchvision.models)</a>
</li>
      <li><a href="torch.html#torch.result_type">result_type() (in module torch)</a>
</li>
      <li><a href="autograd.html#torch.Tensor.retain_grad">retain_grad() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.rfft">rfft() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.rfft">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="optim.html#torch.optim.RMSprop">RMSprop (class in torch.optim)</a>
</li>
      <li><a href="nn.html#torch.nn.RNN">RNN (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.RNNBase">RNNBase (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.RNNCell">RNNCell (class in torch.nn)</a>
</li>
      <li><a href="torchvision/ops.html#torchvision.ops.roi_align">roi_align() (in module torchvision.ops)</a>
</li>
      <li><a href="torchvision/ops.html#torchvision.ops.roi_pool">roi_pool() (in module torchvision.ops)</a>
</li>
      <li><a href="torchvision/ops.html#torchvision.ops.RoIAlign">RoIAlign (class in torchvision.ops)</a>
</li>
      <li><a href="torchvision/ops.html#torchvision.ops.RoIPool">RoIPool (class in torchvision.ops)</a>
</li>
      <li><a href="torch.html#torch.roll">roll() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.roll">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.rot90">rot90() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.rot90">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.rotate">rotate() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torch.html#torch.round">round() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.round">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.round_">round_() (torch.Tensor method)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.rpc_async">rpc_async() (in module torch.distributed.rpc)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.rpc_sync">rpc_sync() (in module torch.distributed.rpc)</a>
</li>
      <li><a href="optim.html#torch.optim.Rprop">Rprop (class in torch.optim)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.RRef">RRef (class in torch.distributed.rpc)</a>
</li>
      <li><a href="nn.html#torch.nn.RReLU">RReLU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.rrelu">rrelu() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.rrelu_">rrelu_() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.beta.Beta.rsample">rsample() (torch.distributions.beta.Beta method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.rsample">(torch.distributions.cauchy.Cauchy method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.rsample">(torch.distributions.dirichlet.Dirichlet method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.rsample">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.rsample">(torch.distributions.exponential.Exponential method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.rsample">(torch.distributions.fishersnedecor.FisherSnedecor method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.rsample">(torch.distributions.gamma.Gamma method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.rsample">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.rsample">(torch.distributions.laplace.Laplace method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.rsample">(torch.distributions.multivariate_normal.MultivariateNormal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.rsample">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.rsample">(torch.distributions.studentT.StudentT method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.rsample">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.rsample">(torch.distributions.uniform.Uniform method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.rsqrt">rsqrt() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.rsqrt">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.rsqrt_">rsqrt_() (torch.Tensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.sample">sample() (torch.distributions.bernoulli.Bernoulli method)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.sample">(torch.distributions.binomial.Binomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.sample">(torch.distributions.categorical.Categorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.sample">(torch.distributions.distribution.Distribution method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.sample">(torch.distributions.geometric.Geometric method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.sample">(torch.distributions.independent.Independent method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.sample">(torch.distributions.multinomial.Multinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.sample">(torch.distributions.negative_binomial.NegativeBinomial method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.sample">(torch.distributions.normal.Normal method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.sample">(torch.distributions.one_hot_categorical.OneHotCategorical method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.sample">(torch.distributions.poisson.Poisson method)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.sample">(torch.distributions.transformed_distribution.TransformedDistribution method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.sample_n">sample_n() (torch.distributions.distribution.Distribution method)</a>
</li>
      <li><a href="data.html#torch.utils.data.Sampler">Sampler (class in torch.utils.data)</a>
</li>
      <li><a href="torch.html#torch.save">save() (in module torch)</a>

      <ul>
        <li><a href="jit.html#torch.jit.save">(in module torch.jit)</a>
</li>
        <li><a href="jit.html#torch.jit.ScriptModule.save">(torch.jit.ScriptModule method)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.autograd.function._ContextMethodMixin.save_for_backward">save_for_backward() (torch.autograd.function._ContextMethodMixin method)</a>
</li>
      <li><a href="torchvision/utils.html#torchvision.utils.save_image">save_image() (in module torchvision.utils)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.SBDataset">SBDataset (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.SBU">SBU (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.Scale">Scale (class in torchvision.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.scale">scale() (torch.distributions.half_cauchy.HalfCauchy property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.scale">(torch.distributions.half_normal.HalfNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.scale">(torch.distributions.log_normal.LogNormal property)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.scale_tril">scale_tril (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.scale_tril">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.comm.scatter">scatter() (in module torch.cuda.comm)</a>

      <ul>
        <li><a href="distributed.html#torch.distributed.scatter">(in module torch.distributed)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.scatter">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.scatter_">scatter_() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.scatter_add">scatter_add() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.scatter_add_">scatter_add_() (torch.Tensor method)</a>
</li>
      <li><a href="jit.html#torch.jit.script">script() (in module torch.jit)</a>
</li>
      <li><a href="jit.html#torch.jit.ScriptFunction">ScriptFunction (class in torch.jit)</a>
</li>
      <li><a href="jit.html#torch.jit.ScriptModule">ScriptModule (class in torch.jit)</a>
</li>
      <li><a href="torch.html#torch.seed">seed() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.seed">(in module torch.cuda)</a>
</li>
        <li><a href="random.html#torch.random.seed">(in module torch.random)</a>, <a href="random.html#torch.random.seed">[1]</a>
</li>
        <li><a href="torch.html#torch._C.Generator.seed">(torch._C.Generator method)</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.seed_all">seed_all() (in module torch.cuda)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.select">select() (torch.Tensor method)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.profile.self_cpu_time_total">self_cpu_time_total() (torch.autograd.profiler.profile property)</a>
</li>
      <li><a href="nn.html#torch.nn.SELU">SELU (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.selu">selu() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributed.html#torch.distributed.send">send() (in module torch.distributed)</a>
</li>
      <li><a href="nn.html#torch.nn.Sequential">Sequential (class in torch.nn)</a>
</li>
      <li><a href="data.html#torch.utils.data.SequentialSampler">SequentialSampler (class in torch.utils.data)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.set_">set_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.set_default_dtype">set_default_dtype() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.set_default_tensor_type">set_default_tensor_type() (in module torch)</a>
</li>
      <li><a href="autograd.html#torch.autograd.set_detect_anomaly">set_detect_anomaly (class in torch.autograd)</a>
</li>
      <li><a href="cuda.html#torch.cuda.set_device">set_device() (in module torch.cuda)</a>
</li>
      <li><a href="hub.html#torch.hub.set_dir">set_dir() (in module torch.hub)</a>
</li>
      <li><a href="torch.html#torch.set_flush_denormal">set_flush_denormal() (in module torch)</a>
</li>
      <li><a href="autograd.html#torch.autograd.set_grad_enabled">set_grad_enabled (class in torch.autograd)</a>
</li>
      <li><a href="torchvision/index.html#torchvision.set_image_backend">set_image_backend() (in module torchvision)</a>
</li>
      <li><a href="torch.html#torch.set_num_interop_threads">set_num_interop_threads() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.set_num_threads">set_num_threads() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.set_printoptions">set_printoptions() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.set_rng_state">set_rng_state() (in module torch)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.set_rng_state">(in module torch.cuda)</a>
</li>
        <li><a href="random.html#torch.random.set_rng_state">(in module torch.random)</a>, <a href="random.html#torch.random.set_rng_state">[1]</a>
</li>
      </ul></li>
      <li><a href="cuda.html#torch.cuda.set_rng_state_all">set_rng_state_all() (in module torch.cuda)</a>
</li>
      <li><a href="multiprocessing.html#torch.multiprocessing.set_sharing_strategy">set_sharing_strategy() (in module torch.multiprocessing)</a>
</li>
      <li><a href="torch.html#torch._C.Generator.set_state">set_state() (torch._C.Generator method)</a>
</li>
      <li><a href="onnx.html#torch.onnx.set_training">set_training() (in module torch.onnx)</a>
</li>
      <li><a href="torchvision/index.html#torchvision.set_video_backend">set_video_backend() (in module torchvision)</a>
</li>
      <li><a href="optim.html#torch.optim.SGD">SGD (class in torch.optim)</a>
</li>
      <li><a href="onnx.html#torch.onnx.operators.shape_as_tensor">shape_as_tensor() (in module torch.onnx.operators)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.share_memory_">share_memory_() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.share_memory_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="storage.html#torch.FloatStorage.short">short() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.short">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="__config__.html#torch.__config__.show">show() (in module torch.__config__)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.shufflenet_v2_x0_5">shufflenet_v2_x0_5() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.shufflenet_v2_x1_0">shufflenet_v2_x1_0() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.shufflenet_v2_x1_5">shufflenet_v2_x1_5() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.shufflenet_v2_x2_0">shufflenet_v2_x2_0() (in module torchvision.models)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.shutdown">shutdown() (in module torch.distributed.rpc)</a>
</li>
      <li><a href="nn.html#torch.nn.Sigmoid">Sigmoid (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.sigmoid">sigmoid() (in module torch)</a>

      <ul>
        <li><a href="nn.functional.html#torch.nn.functional.sigmoid">(in module torch.nn.functional)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.sigmoid">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sigmoid_">sigmoid_() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.SigmoidTransform">SigmoidTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="torch.html#torch.sign">sign() (in module torch)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.transforms.Transform.sign">(torch.distributions.transforms.Transform property)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.sign">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sign_">sign_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.sin">sin() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sin">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sin_">sin_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.sinh">sinh() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sinh">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sinh_">sinh_() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.size">size() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.size">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.size">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.slogdet">slogdet() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.slogdet">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.smooth_l1_loss">smooth_l1_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.SmoothL1Loss">SmoothL1Loss (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.quasirandom.SobolEngine">SobolEngine (class in torch.quasirandom)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.soft_margin_loss">soft_margin_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.SoftMarginLoss">SoftMarginLoss (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.Softmax">Softmax (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.softmax">softmax() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Softmax2d">Softmax2d (class in torch.nn)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.SoftmaxTransform">SoftmaxTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.Softmin">Softmin (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.softmin">softmin() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Softplus">Softplus (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.softplus">softplus() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Softshrink">Softshrink (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.softshrink">softshrink() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Softsign">Softsign (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.softsign">softsign() (in module torch.nn.functional)</a>
</li>
      <li><a href="torch.html#torch.solve">solve() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.solve">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torch.html#torch.sort">sort() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sort">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.spadd">spadd() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.sparse_">sparse_() (in module torch.nn.init)</a>
</li>
      <li><a href="torch.html#torch.sparse_coo_tensor">sparse_coo_tensor() (in module torch)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.sparse_dim">sparse_dim() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.sparse_mask">sparse_mask() (torch.Tensor method)</a>
</li>
      <li><a href="optim.html#torch.optim.SparseAdam">SparseAdam (class in torch.optim)</a>
</li>
      <li><a href="multiprocessing.html#torch.multiprocessing.spawn">spawn() (in module torch.multiprocessing)</a>
</li>
      <li><a href="multiprocessing.html#torch.multiprocessing.SpawnContext">SpawnContext (class in torch.multiprocessing)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.spectral_norm">spectral_norm() (in module torch.nn.utils)</a>
</li>
      <li><a href="torch.html#torch.split">split() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.split">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.spmm">spmm() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="torch.html#torch.sqrt">sqrt() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sqrt">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sqrt_">sqrt_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.squeeze">squeeze() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.squeeze">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.squeeze_">squeeze_() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.squeezenet1_0">squeezenet1_0() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.squeezenet1_1">squeezenet1_1() (in module torchvision.models)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.sspaddmm">sspaddmm() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.sspmm">sspmm() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.constraints.stack">stack (in module torch.distributions.constraints)</a>
</li>
      <li><a href="torch.html#torch.stack">stack() (in module torch)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.StackTransform">StackTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.state_dict">state_dict() (torch.nn.Module method)</a>

      <ul>
        <li><a href="optim.html#torch.optim.lr_scheduler.LambdaLR.state_dict">(torch.optim.lr_scheduler.LambdaLR method)</a>
</li>
        <li><a href="optim.html#torch.optim.lr_scheduler.MultiplicativeLR.state_dict">(torch.optim.lr_scheduler.MultiplicativeLR method)</a>
</li>
        <li><a href="optim.html#torch.optim.Optimizer.state_dict">(torch.optim.Optimizer method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.std">std() (in module torch)</a>, <a href="torch.html#torch.std">[1]</a>, <a href="torch.html#torch.std">[2]</a>, <a href="torch.html#torch.std">[3]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.std">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.std_mean">std_mean() (in module torch)</a>, <a href="torch.html#torch.std_mean">[1]</a>
</li>
      <li><a href="distributions.html#torch.distributions.distribution.Distribution.stddev">stddev() (torch.distributions.distribution.Distribution property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.stddev">(torch.distributions.exponential.Exponential property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.stddev">(torch.distributions.gumbel.Gumbel property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.stddev">(torch.distributions.laplace.Laplace property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.stddev">(torch.distributions.normal.Normal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.stddev">(torch.distributions.uniform.Uniform property)</a>
</li>
      </ul></li>
      <li><a href="rpc.html#torch.distributed.optim.DistributedOptimizer.step">step() (torch.distributed.optim.DistributedOptimizer method)</a>

      <ul>
        <li><a href="optim.html#torch.optim.Adadelta.step">(torch.optim.Adadelta method)</a>
</li>
        <li><a href="optim.html#torch.optim.Adagrad.step">(torch.optim.Adagrad method)</a>
</li>
        <li><a href="optim.html#torch.optim.Adam.step">(torch.optim.Adam method)</a>
</li>
        <li><a href="optim.html#torch.optim.Adamax.step">(torch.optim.Adamax method)</a>
</li>
        <li><a href="optim.html#torch.optim.AdamW.step">(torch.optim.AdamW method)</a>
</li>
        <li><a href="optim.html#torch.optim.ASGD.step">(torch.optim.ASGD method)</a>
</li>
        <li><a href="optim.html#torch.optim.LBFGS.step">(torch.optim.LBFGS method)</a>
</li>
        <li><a href="optim.html#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step">(torch.optim.lr_scheduler.CosineAnnealingWarmRestarts method)</a>
</li>
        <li><a href="optim.html#torch.optim.Optimizer.step">(torch.optim.Optimizer method)</a>
</li>
        <li><a href="optim.html#torch.optim.RMSprop.step">(torch.optim.RMSprop method)</a>
</li>
        <li><a href="optim.html#torch.optim.Rprop.step">(torch.optim.Rprop method)</a>
</li>
        <li><a href="optim.html#torch.optim.SGD.step">(torch.optim.SGD method)</a>
</li>
        <li><a href="optim.html#torch.optim.SparseAdam.step">(torch.optim.SparseAdam method)</a>
</li>
      </ul></li>
      <li><a href="optim.html#torch.optim.lr_scheduler.StepLR">StepLR (class in torch.optim.lr_scheduler)</a>
</li>
      <li><a href="torch.html#torch.stft">stft() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.stft">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.transforms.StickBreakingTransform">StickBreakingTransform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.STL10">STL10 (class in torchvision.datasets)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.storage">storage() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.storage_offset">storage_offset() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.storage_type">storage_type() (torch.Tensor method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Stream">Stream (class in torch.cuda)</a>
</li>
      <li><a href="cuda.html#torch.cuda.stream">stream() (in module torch.cuda)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.stride">stride() (torch.Tensor method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.studentT.StudentT">StudentT (class in torch.distributions.studentT)</a>
</li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.sub">sub() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sub">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.sub_">sub_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.sub_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="data.html#torch.utils.data.Subset">Subset (class in torch.utils.data)</a>
</li>
      <li><a href="data.html#torch.utils.data.SubsetRandomSampler">SubsetRandomSampler (class in torch.utils.data)</a>
</li>
      <li><a href="torch.html#torch.sum">sum() (in module torch)</a>, <a href="torch.html#torch.sum">[1]</a>, <a href="torch.html#torch.sum">[2]</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.sum">(in module torch.sparse)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.sum">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.sum_to_size">sum_to_size() (torch.Tensor method)</a>
</li>
      <li><a href="tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter">SummaryWriter (class in torch.utils.tensorboard.writer)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.support">support (torch.distributions.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.support">(torch.distributions.beta.Beta attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.support">(torch.distributions.cauchy.Cauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.support">(torch.distributions.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.support">(torch.distributions.exponential.Exponential attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.support">(torch.distributions.fishersnedecor.FisherSnedecor attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.support">(torch.distributions.gamma.Gamma attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.support">(torch.distributions.geometric.Geometric attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.support">(torch.distributions.gumbel.Gumbel attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.support">(torch.distributions.half_cauchy.HalfCauchy attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.support">(torch.distributions.half_normal.HalfNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.support">(torch.distributions.laplace.Laplace attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.support">(torch.distributions.log_normal.LogNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.support">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.support">(torch.distributions.multivariate_normal.MultivariateNormal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.support">(torch.distributions.negative_binomial.NegativeBinomial attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.support">(torch.distributions.normal.Normal attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.support">(torch.distributions.one_hot_categorical.OneHotCategorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.support">(torch.distributions.poisson.Poisson attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.support">(torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.support">(torch.distributions.relaxed_bernoulli.RelaxedBernoulli attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.support">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.support">(torch.distributions.studentT.StudentT attribute)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.support">(torch.distributions.weibull.Weibull attribute)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.binomial.Binomial.support">support() (torch.distributions.binomial.Binomial property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.support">(torch.distributions.categorical.Categorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.support">(torch.distributions.distribution.Distribution property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.support">(torch.distributions.independent.Independent property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.support">(torch.distributions.multinomial.Multinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.support">(torch.distributions.pareto.Pareto property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.support">(torch.distributions.transformed_distribution.TransformedDistribution property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.support">(torch.distributions.uniform.Uniform property)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.svd">svd() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.svd">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.SVHN">SVHN (class in torchvision.datasets)</a>
</li>
      <li><a href="quantization.html#torch.quantization.swap_module">swap_module() (in module torch.quantization)</a>
</li>
      <li><a href="torch.html#torch.symeig">symeig() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.symeig">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.SyncBatchNorm">SyncBatchNorm (class in torch.nn)</a>
</li>
      <li><a href="cuda.html#torch.cuda.synchronize">synchronize() (in module torch.cuda)</a>

      <ul>
        <li><a href="cuda.html#torch.cuda.Event.synchronize">(torch.cuda.Event method)</a>
</li>
        <li><a href="cuda.html#torch.cuda.Stream.synchronize">(torch.cuda.Stream method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="T">T</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.T">T (torch.Tensor attribute)</a>
</li>
      <li><a href="torch.html#torch.t">t() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.t">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.t_">t_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.t_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="autograd.html#torch.autograd.profiler.profile.table">table() (torch.autograd.profiler.profile method)</a>
</li>
      <li><a href="torch.html#torch.take">take() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.take">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.tan">tan() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.tan">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.tan_">tan_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Tanh">Tanh (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.tanh">tanh() (in module torch)</a>

      <ul>
        <li><a href="nn.functional.html#torch.nn.functional.tanh">(in module torch.nn.functional)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.tanh">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.tanh_">tanh_() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Tanhshrink">Tanhshrink (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.tanhshrink">tanhshrink() (in module torch.nn.functional)</a>
</li>
      <li><a href="distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature">temperature() (torch.distributions.relaxed_bernoulli.RelaxedBernoulli property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.temperature">(torch.distributions.relaxed_categorical.RelaxedOneHotCategorical property)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.ten_crop">ten_crop() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.TenCrop">TenCrop (class in torchvision.transforms)</a>
</li>
      <li><a href="tensors.html#torch.Tensor">Tensor (class in torch)</a>
</li>
      <li><a href="torch.html#torch.tensor">tensor() (in module torch)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.bernoulli_">Tensor.bernoulli_() (in module torch)</a>, <a href="tensors.html#torch.Tensor.bernoulli_">[1]</a>
</li>
      <li><a href="tensors.html#torch.Tensor.to">Tensor.to() (in module torch)</a>, <a href="tensors.html#torch.Tensor.to">[1]</a>, <a href="tensors.html#torch.Tensor.to">[2]</a>
</li>
      <li><a href="data.html#torch.utils.data.TensorDataset">TensorDataset (class in torch.utils.data)</a>
</li>
      <li><a href="torch.html#torch.tensordot">tensordot() (in module torch)</a>
</li>
      <li><a href="nn.html#torch.nn.Threshold">Threshold (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.threshold">threshold() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.threshold_">threshold_() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.to">to() (torch.nn.Module method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.to">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.to_dense">to_dense() (torch.sparse.FloatTensor method)</a>
</li>
      <li><a href="dlpack.html#torch.utils.dlpack.to_dlpack">to_dlpack() (in module torch.utils.dlpack)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.to_grayscale">to_grayscale() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="rpc.html#torch.distributed.rpc.RRef.to_here">to_here() (torch.distributed.rpc.RRef method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.to_mkldnn">to_mkldnn() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.to_pil_image">to_pil_image() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.to_sparse">to_sparse() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.to_tensor">to_tensor() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.tolist">tolist() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.tolist">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.ToPILImage">ToPILImage (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.topk">topk() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.topk">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#module-torch">torch (module)</a>
</li>
      <li><a href="__config__.html#module-torch.__config__">torch.__config__ (module)</a>
</li>
      <li><a href="autograd.html#module-torch.autograd">torch.autograd (module)</a>
</li>
      <li><a href="cuda.html#module-torch.cuda">torch.cuda (module)</a>
</li>
      <li><a href="tensor_attributes.html#torch.torch.device">torch.device (class in torch)</a>
</li>
      <li><a href="distributed.html#module-torch.distributed">torch.distributed (module)</a>
</li>
      <li><a href="rpc.html#module-torch.distributed.autograd">torch.distributed.autograd (module)</a>
</li>
      <li><a href="distributed.html#module-torch.distributed.launch">torch.distributed.launch (module)</a>
</li>
      <li><a href="rpc.html#module-torch.distributed.optim">torch.distributed.optim (module)</a>
</li>
      <li><a href="rpc.html#module-torch.distributed.rpc">torch.distributed.rpc (module)</a>, <a href="rpc.html#module-torch.distributed.rpc">[1]</a>
</li>
      <li><a href="distributions.html#module-torch.distributions">torch.distributions (module)</a>
</li>
      <li><a href="distributions.html#module-torch.distributions.constraint_registry">torch.distributions.constraint_registry (module)</a>
</li>
      <li><a href="distributions.html#module-torch.distributions.constraints">torch.distributions.constraints (module)</a>
</li>
      <li><a href="distributions.html#module-torch.distributions.kl">torch.distributions.kl (module)</a>
</li>
      <li><a href="distributions.html#module-torch.distributions.transforms">torch.distributions.transforms (module)</a>
</li>
      <li><a href="tensor_attributes.html#torch.torch.dtype">torch.dtype (class in torch)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="type_info.html#torch.torch.finfo">torch.finfo (class in torch)</a>
</li>
      <li><a href="hub.html#module-torch.hub">torch.hub (module)</a>
</li>
      <li><a href="type_info.html#torch.torch.iinfo">torch.iinfo (class in torch)</a>
</li>
      <li><a href="jit.html#module-torch.jit">torch.jit (module)</a>
</li>
      <li><a href="jit_builtin_functions.html#module-torch.jit.supported_ops">torch.jit.supported_ops (module)</a>
</li>
      <li><a href="tensor_attributes.html#torch.torch.layout">torch.layout (class in torch)</a>
</li>
      <li><a href="multiprocessing.html#module-torch.multiprocessing">torch.multiprocessing (module)</a>
</li>
      <li><a href="nn.html#module-torch.nn">torch.nn (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.intrinsic">torch.nn.intrinsic (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.intrinsic.qat">torch.nn.intrinsic.qat (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.intrinsic.quantized">torch.nn.intrinsic.quantized (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.qat">torch.nn.qat (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.quantized">torch.nn.quantized (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.quantized.dynamic">torch.nn.quantized.dynamic (module)</a>
</li>
      <li><a href="quantization.html#module-torch.nn.quantized.functional">torch.nn.quantized.functional (module)</a>
</li>
      <li><a href="onnx.html#module-torch.onnx">torch.onnx (module)</a>
</li>
      <li><a href="optim.html#module-torch.optim">torch.optim (module)</a>
</li>
      <li><a href="quantization.html#module-torch.quantization">torch.quantization (module)</a>
</li>
      <li><a href="random.html#module-torch.random">torch.random (module)</a>
</li>
      <li><a href="data.html#module-torch.utils.data">torch.utils.data (module)</a>
</li>
      <li><a href="model_zoo.html#module-torch.utils.model_zoo">torch.utils.model_zoo (module)</a>
</li>
      <li><a href="torchvision/index.html#module-torchvision">torchvision (module)</a>
</li>
      <li><a href="torchvision/transforms.html#module-torchvision.transforms.functional">torchvision.transforms.functional (module)</a>
</li>
      <li><a href="autograd.html#torch.autograd.profiler.profile.total_average">total_average() (torch.autograd.profiler.profile method)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.ToTensor">ToTensor (class in torchvision.transforms)</a>
</li>
      <li><a href="torch.html#torch.trace">trace() (in module torch)</a>

      <ul>
        <li><a href="jit.html#torch.jit.trace">(in module torch.jit)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.trace">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="jit.html#torch.jit.trace_module">trace_module() (in module torch.jit)</a>
</li>
      <li><a href="nn.html#torch.nn.Module.train">train() (torch.nn.Module method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transforms.Transform">Transform (class in torch.distributions.transforms)</a>
</li>
      <li><a href="distributions.html#torch.distributions.transformed_distribution.TransformedDistribution">TransformedDistribution (class in torch.distributions.transformed_distribution)</a>
</li>
      <li><a href="nn.html#torch.nn.Transformer">Transformer (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.TransformerDecoder">TransformerDecoder (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.TransformerDecoderLayer">TransformerDecoderLayer (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.TransformerEncoder">TransformerEncoder (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.TransformerEncoderLayer">TransformerEncoderLayer (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.transpose">transpose() (in module torch)</a>

      <ul>
        <li><a href="sparse.html#torch.sparse.FloatTensor.transpose">(torch.sparse.FloatTensor method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.transpose">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="sparse.html#torch.sparse.FloatTensor.transpose_">transpose_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.transpose_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.trapz">trapz() (in module torch)</a>, <a href="torch.html#torch.trapz">[1]</a>, <a href="torch.html#torch.trapz">[2]</a>
</li>
      <li><a href="torch.html#torch.triangular_solve">triangular_solve() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.triangular_solve">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.tril">tril() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.tril">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.tril_">tril_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.tril_indices">tril_indices() (in module torch)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.triplet_margin_loss">triplet_margin_loss() (in module torch.nn.functional)</a>
</li>
      <li><a href="nn.html#torch.nn.TripletMarginLoss">TripletMarginLoss (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.triu">triu() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.triu">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.triu_">triu_() (torch.Tensor method)</a>
</li>
      <li><a href="torch.html#torch.triu_indices">triu_indices() (in module torch)</a>
</li>
      <li><a href="torch.html#torch.trunc">trunc() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.trunc">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.trunc_">trunc_() (torch.Tensor method)</a>
</li>
      <li><a href="storage.html#torch.FloatStorage.type">type() (torch.FloatStorage method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.Module.type">(torch.nn.Module method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.type">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="tensors.html#torch.Tensor.type_as">type_as() (torch.Tensor method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="U">U</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="torchvision/datasets.html#torchvision.datasets.UCF101">UCF101 (class in torchvision.datasets)</a>
</li>
      <li><a href="torch.html#torch.unbind">unbind() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.unbind">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="named_tensor.html#torch.Tensor.unflatten">unflatten() (torch.Tensor method)</a>
</li>
      <li><a href="nn.html#torch.nn.Unfold">Unfold (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.unfold">unfold() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.unfold">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="distributions.html#torch.distributions.uniform.Uniform">Uniform (class in torch.distributions.uniform)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.uniform_">uniform_() (in module torch.nn.init)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.uniform_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.unique">unique() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.unique">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.unique_consecutive">unique_consecutive() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.unique_consecutive">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.unsqueeze">unsqueeze() (in module torch)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.unsqueeze">(torch.Tensor method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="tensors.html#torch.Tensor.unsqueeze_">unsqueeze_() (torch.Tensor method)</a>
</li>
      <li><a href="jit.html#torch.jit.unused">unused() (in module torch.jit)</a>
</li>
      <li><a href="nn.html#torch.nn.ModuleDict.update">update() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.update">(torch.nn.ParameterDict method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Upsample">Upsample (class in torch.nn)</a>
</li>
      <li><a href="nn.functional.html#torch.nn.functional.upsample">upsample() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.upsample">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.upsample_bilinear">upsample_bilinear() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.upsample_bilinear">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.functional.html#torch.nn.functional.upsample_nearest">upsample_nearest() (in module torch.nn.functional)</a>

      <ul>
        <li><a href="quantization.html#torch.nn.quantized.functional.upsample_nearest">(in module torch.nn.quantized.functional)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.UpsamplingBilinear2d">UpsamplingBilinear2d (class in torch.nn)</a>
</li>
      <li><a href="nn.html#torch.nn.UpsamplingNearest2d">UpsamplingNearest2d (class in torch.nn)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.USPS">USPS (class in torchvision.datasets)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="V">V</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.ModuleDict.values">values() (torch.nn.ModuleDict method)</a>

      <ul>
        <li><a href="nn.html#torch.nn.ParameterDict.values">(torch.nn.ParameterDict method)</a>
</li>
        <li><a href="tensors.html#torch.Tensor.values">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.var">var() (in module torch)</a>, <a href="torch.html#torch.var">[1]</a>, <a href="torch.html#torch.var">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.var">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="torch.html#torch.var_mean">var_mean() (in module torch)</a>, <a href="torch.html#torch.var_mean">[1]</a>, <a href="torch.html#torch.var_mean">[2]</a>
</li>
      <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.variance">variance (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute)</a>
</li>
      <li><a href="distributions.html#torch.distributions.bernoulli.Bernoulli.variance">variance() (torch.distributions.bernoulli.Bernoulli property)</a>

      <ul>
        <li><a href="distributions.html#torch.distributions.beta.Beta.variance">(torch.distributions.beta.Beta property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.binomial.Binomial.variance">(torch.distributions.binomial.Binomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.categorical.Categorical.variance">(torch.distributions.categorical.Categorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.cauchy.Cauchy.variance">(torch.distributions.cauchy.Cauchy property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.dirichlet.Dirichlet.variance">(torch.distributions.dirichlet.Dirichlet property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.distribution.Distribution.variance">(torch.distributions.distribution.Distribution property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.exponential.Exponential.variance">(torch.distributions.exponential.Exponential property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.variance">(torch.distributions.fishersnedecor.FisherSnedecor property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gamma.Gamma.variance">(torch.distributions.gamma.Gamma property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.geometric.Geometric.variance">(torch.distributions.geometric.Geometric property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.gumbel.Gumbel.variance">(torch.distributions.gumbel.Gumbel property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_cauchy.HalfCauchy.variance">(torch.distributions.half_cauchy.HalfCauchy property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.half_normal.HalfNormal.variance">(torch.distributions.half_normal.HalfNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.independent.Independent.variance">(torch.distributions.independent.Independent property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.laplace.Laplace.variance">(torch.distributions.laplace.Laplace property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.variance">(torch.distributions.log_normal.LogNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multinomial.Multinomial.variance">(torch.distributions.multinomial.Multinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.variance">(torch.distributions.multivariate_normal.MultivariateNormal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.negative_binomial.NegativeBinomial.variance">(torch.distributions.negative_binomial.NegativeBinomial property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.normal.Normal.variance">(torch.distributions.normal.Normal property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.variance">(torch.distributions.one_hot_categorical.OneHotCategorical property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.pareto.Pareto.variance">(torch.distributions.pareto.Pareto property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.poisson.Poisson.variance">(torch.distributions.poisson.Poisson property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.studentT.StudentT.variance">(torch.distributions.studentT.StudentT property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.uniform.Uniform.variance">(torch.distributions.uniform.Uniform property)</a>
</li>
        <li><a href="distributions.html#torch.distributions.weibull.Weibull.variance">(torch.distributions.weibull.Weibull property)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.utils.vector_to_parameters">vector_to_parameters() (in module torch.nn.utils)</a>
</li>
      <li><a href="cpp_extension.html#torch.utils.cpp_extension.verify_ninja_availability">verify_ninja_availability() (in module torch.utils.cpp_extension)</a>
</li>
      <li><a href="torchvision/transforms.html#torchvision.transforms.functional.vflip">vflip() (in module torchvision.transforms.functional)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg11">vgg11() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg11_bn">vgg11_bn() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg13">vgg13() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg13_bn">vgg13_bn() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg16">vgg16() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg16_bn">vgg16_bn() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg19">vgg19() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.vgg19_bn">vgg19_bn() (in module torchvision.models)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.view">view() (torch.Tensor method)</a>
</li>
      <li><a href="tensors.html#torch.Tensor.view_as">view_as() (torch.Tensor method)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.VOCDetection">VOCDetection (class in torchvision.datasets)</a>
</li>
      <li><a href="torchvision/datasets.html#torchvision.datasets.VOCSegmentation">VOCSegmentation (class in torchvision.datasets)</a>
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<h2 id="W">W</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="cuda.html#torch.cuda.Event.wait">wait() (torch.cuda.Event method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Stream.wait_event">wait_event() (torch.cuda.Stream method)</a>
</li>
      <li><a href="cuda.html#torch.cuda.Stream.wait_stream">wait_stream() (torch.cuda.Stream method)</a>
</li>
      <li><a href="distributions.html#torch.distributions.weibull.Weibull">Weibull (class in torch.distributions.weibull)</a>
</li>
      <li><a href="nn.html#torch.nn.utils.weight_norm">weight_norm() (in module torch.nn.utils)</a>
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  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="data.html#torch.utils.data.WeightedRandomSampler">WeightedRandomSampler (class in torch.utils.data)</a>
</li>
      <li><a href="torch.html#torch.where">where() (in module torch)</a>, <a href="torch.html#torch.where">[1]</a>, <a href="torch.html#torch.where">[2]</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.where">(torch.Tensor method)</a>
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      <li><a href="torchvision/models.html#torchvision.models.wide_resnet101_2">wide_resnet101_2() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/models.html#torchvision.models.wide_resnet50_2">wide_resnet50_2() (in module torchvision.models)</a>
</li>
      <li><a href="torchvision/io.html#torchvision.io.write_video">write_video() (in module torchvision.io)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="X">X</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.init.html#torch.nn.init.xavier_normal_">xavier_normal_() (in module torch.nn.init)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.init.html#torch.nn.init.xavier_uniform_">xavier_uniform_() (in module torch.nn.init)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="Z">Z</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="sparse.html#torch.sparse.FloatTensor.zero_">zero_() (torch.sparse.FloatTensor method)</a>

      <ul>
        <li><a href="tensors.html#torch.Tensor.zero_">(torch.Tensor method)</a>
</li>
      </ul></li>
      <li><a href="nn.html#torch.nn.Module.zero_grad">zero_grad() (torch.nn.Module method)</a>

      <ul>
        <li><a href="optim.html#torch.optim.Optimizer.zero_grad">(torch.optim.Optimizer method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nn.html#torch.nn.ZeroPad2d">ZeroPad2d (class in torch.nn)</a>
</li>
      <li><a href="torch.html#torch.zeros">zeros() (in module torch)</a>
</li>
      <li><a href="nn.init.html#torch.nn.init.zeros_">zeros_() (in module torch.nn.init)</a>
</li>
      <li><a href="torch.html#torch.zeros_like">zeros_like() (in module torch)</a>
</li>
  </ul></td>
</tr></table>


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