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< div class ="version ">
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- < a href ='http://pytorch.org/docs/versions.html '> 1.7.0a0+fc26830 ▼</ a >
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+ < a href ='http://pytorch.org/docs/versions.html '> 1.7.0a0+69d74c8 ▼</ a >
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</ div >
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@@ -565,7 +565,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="k "> return</ span > < span class ="n "> module</ span > < span class ="o "> +</ span > < span class ="n "> class_name</ span >
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- < span class ="k "> def</ span > < span class ="nf "> is_tensor</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ):</ span >
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+ < div class =" viewcode-block " id =" is_tensor " > < a class =" viewcode-back " href =" ../generated/torch.is_tensor.html#torch.is_tensor " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> is_tensor</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ):</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Returns True if `obj` is a PyTorch tensor.</ span >
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< span class ="sd "> Note that this function is simply doing ``isinstance(obj, Tensor)``.</ span >
@@ -576,19 +576,19 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="sd "> Args:</ span >
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< span class ="sd "> obj (Object): Object to test</ span >
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< span class ="sd "> """</ span >
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- < span class ="k "> return</ span > < span class ="nb "> isinstance</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ,</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> Tensor</ span > < span class ="p "> )</ span >
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+ < span class ="k "> return</ span > < span class ="nb "> isinstance</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ,</ span > < span class ="n "> torch</ span > < span class ="o "> .</ span > < span class ="n "> Tensor</ span > < span class ="p "> )</ span > </ div >
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- < span class ="k "> def</ span > < span class ="nf "> is_storage</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ):</ span >
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+ < div class =" viewcode-block " id =" is_storage " > < a class =" viewcode-back " href =" ../generated/torch.is_storage.html#torch.is_storage " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> is_storage</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> ):</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Returns True if `obj` is a PyTorch storage object.</ span >
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< span class ="sd "> Args:</ span >
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< span class ="sd "> obj (Object): Object to test</ span >
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< span class ="sd "> """</ span >
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- < span class ="k "> return</ span > < span class ="nb "> type</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> )</ span > < span class ="ow "> in</ span > < span class ="n "> _storage_classes</ span >
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+ < span class ="k "> return</ span > < span class ="nb "> type</ span > < span class ="p "> (</ span > < span class ="n "> obj</ span > < span class ="p "> )</ span > < span class ="ow "> in</ span > < span class ="n "> _storage_classes</ span > </ div >
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- < span class ="k "> def</ span > < span class ="nf "> set_default_tensor_type</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> ):</ span >
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+ < div class =" viewcode-block " id =" set_default_tensor_type " > < a class =" viewcode-back " href =" ../generated/torch.set_default_tensor_type.html#torch.set_default_tensor_type " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> set_default_tensor_type</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> ):</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Sets the default ``torch.Tensor`` type to floating point tensor type</ span >
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< span class ="sd "> ``t``. This type will also be used as default floating point type for</ span >
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< span class ="sd "> type inference in :func:`torch.tensor`.</ span >
@@ -609,10 +609,10 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="sd "> """</ span >
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< span class ="k "> if</ span > < span class ="nb "> isinstance</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> ,</ span > < span class ="n "> _string_classes</ span > < span class ="p "> ):</ span >
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< span class ="n "> t</ span > < span class ="o "> =</ span > < span class ="n "> _import_dotted_name</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> )</ span >
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- < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_default_tensor_type</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> )</ span >
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+ < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_default_tensor_type</ span > < span class ="p "> (</ span > < span class ="n "> t</ span > < span class ="p "> )</ span > </ div >
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- < span class ="k "> def</ span > < span class ="nf "> set_default_dtype</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> ):</ span >
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+ < div class =" viewcode-block " id =" set_default_dtype " > < a class =" viewcode-back " href =" ../generated/torch.set_default_dtype.html#torch.set_default_dtype " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> set_default_dtype</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> ):</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Sets the default floating point dtype to :attr:`d`.</ span >
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< span class ="sd "> This dtype is:</ span >
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@@ -640,9 +640,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="sd "> torch.complex128</ span >
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< span class ="sd "> """</ span >
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- < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_default_dtype</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> )</ span >
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+ < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_default_dtype</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> )</ span > </ div >
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- < span class ="k "> def</ span > < span class ="nf "> set_deterministic</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> ):</ span >
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+ < div class =" viewcode-block " id =" set_deterministic " > < a class =" viewcode-back " href =" ../generated/torch.set_deterministic.html#torch.set_deterministic " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> set_deterministic</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> ):</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """ Sets whether native PyTorch operations must use deterministic</ span >
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< span class ="sd "> algorithms. When True, operations without deterministic algorithms</ span >
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< span class ="sd "> will throw a :class:RuntimeError when called.</ span >
@@ -712,13 +712,13 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="sd "> d (:class:`bool`): If True, force operations to be deterministic.</ span >
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< span class ="sd "> If False, allow non-deterministic operations.</ span >
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< span class ="sd "> """</ span >
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- < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_deterministic</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> )</ span >
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+ < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_deterministic</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> )</ span > </ div >
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- < span class ="k "> def</ span > < span class ="nf "> is_deterministic</ span > < span class ="p "> ():</ span >
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+ < div class =" viewcode-block " id =" is_deterministic " > < a class =" viewcode-back " href =" ../generated/torch.is_deterministic.html#torch.is_deterministic " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> is_deterministic</ span > < span class ="p "> ():</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Returns True if the global deterministic flag is turned on. Refer to</ span >
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< span class ="sd "> :func:`torch.set_deterministic` documentation for more details.</ span >
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< span class ="sd "> """</ span >
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- < span class ="k "> return</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _get_deterministic</ span > < span class ="p "> ()</ span >
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+ < span class ="k "> return</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _get_deterministic</ span > < span class ="p "> ()</ span > </ div >
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< span class ="c1 "> ################################################################################</ span >
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< span class ="c1 "> # Define Storage and Tensor classes</ span >
@@ -732,8 +732,8 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="k "> pass</ span >
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- < div class =" viewcode-block " id =" FloatStorage " > < a class =" viewcode-back " href =" ../storage.html#torch.FloatStorage " > [docs] </ a > < span class ="k "> class</ span > < span class ="nc "> FloatStorage</ span > < span class ="p "> (</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> FloatStorageBase</ span > < span class ="p "> ,</ span > < span class ="n "> _StorageBase</ span > < span class ="p "> ):</ span >
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- < span class ="k "> pass</ span > </ div >
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+ < span class ="k "> class</ span > < span class ="nc "> FloatStorage</ span > < span class ="p "> (</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> FloatStorageBase</ span > < span class ="p "> ,</ span > < span class ="n "> _StorageBase</ span > < span class ="p "> ):</ span >
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+ < span class ="k "> pass</ span >
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< span class ="k "> class</ span > < span class ="nc "> HalfStorage</ span > < span class ="p "> (</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> HalfStorageBase</ span > < span class ="p "> ,</ span > < span class ="n "> _StorageBase</ span > < span class ="p "> ):</ span >
@@ -896,9 +896,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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< span class ="k "> del</ span > < span class ="n "> _torch_docs</ span > < span class ="p "> ,</ span > < span class ="n "> _tensor_docs</ span > < span class ="p "> ,</ span > < span class ="n "> _storage_docs</ span >
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- < span class ="k "> def</ span > < span class ="nf "> compiled_with_cxx11_abi</ span > < span class ="p "> ():</ span >
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+ < div class =" viewcode-block " id =" compiled_with_cxx11_abi " > < a class =" viewcode-back " href =" ../generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> compiled_with_cxx11_abi</ span > < span class ="p "> ():</ span >
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< span class ="sa "> r</ span > < span class ="sd "> """Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1"""</ span >
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- < span class ="k "> return</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _GLIBCXX_USE_CXX11_ABI</ span >
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+ < span class ="k "> return</ span > < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _GLIBCXX_USE_CXX11_ABI</ span > </ div >
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< span class ="c1 "> # Import the ops "namespace"</ span >
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