192192 < div class ="pytorch-left-menu-search ">
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194194 < div class ="version ">
195- < a href ='https://pytorch.org/docs/versions.html '> master (1.9.0a0+git789de82 ) ▼</ a >
195+ < a href ='https://pytorch.org/docs/versions.html '> master (1.9.0a0+gitabca349 ) ▼</ a >
196196 </ div >
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@@ -685,7 +685,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
685685 < 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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688- < 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 >
688+ < 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 >
689689 < span class ="sa "> r</ span > < span class ="sd "> """Sets the default ``torch.Tensor`` type to floating point tensor type</ span >
690690< span class ="sd "> ``t``. This type will also be used as default floating point type for</ span >
691691< span class ="sd "> type inference in :func:`torch.tensor`.</ span >
@@ -706,10 +706,10 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
706706< span class ="sd "> """</ span >
707707 < 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 >
708708 < 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 >
709- < 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 >
709+ < 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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712- < 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 >
712+ < 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 >
713713 < span class ="sa "> r</ span > < span class ="sd "> """Sets the default floating point dtype to :attr:`d`.</ span >
714714< span class ="sd "> This dtype is:</ span >
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@@ -737,9 +737,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
737737< span class ="sd "> torch.complex128</ span >
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739739< span class ="sd "> """</ span >
740- < 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 >
740+ < 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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742- < span class ="k "> def</ span > < span class ="nf "> use_deterministic_algorithms</ span > < span class ="p "> (</ span > < span class ="n "> mode</ span > < span class ="p "> ):</ span >
742+ < div class =" viewcode-block " id =" use_deterministic_algorithms " > < a class =" viewcode-back " href =" ../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> use_deterministic_algorithms</ span > < span class ="p "> (</ span > < span class ="n "> mode</ span > < span class ="p "> ):</ span >
743743 < span class ="sa "> r</ span > < span class ="sd "> """ Sets whether PyTorch operations must use "deterministic"</ span >
744744< span class ="sd "> algorithms. That is, algorithms which, given the same input, and when</ span >
745745< span class ="sd "> run on the same software and hardware, always produce the same output.</ span >
@@ -853,7 +853,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
853853< span class ="sd "> ...</ span >
854854< span class ="sd "> RuntimeError: index_add_cuda_ does not have a deterministic implementation...</ span >
855855< span class ="sd "> """</ span >
856- < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_deterministic_algorithms</ span > < span class ="p "> (</ span > < span class ="n "> mode</ span > < span class ="p "> )</ span >
856+ < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_deterministic_algorithms</ span > < span class ="p "> (</ span > < span class ="n "> mode</ span > < span class ="p "> )</ span > </ div >
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858858< span class ="k "> def</ span > < span class ="nf "> set_deterministic</ span > < span class ="p "> (</ span > < span class ="n "> d</ span > < span class ="p "> ):</ span >
859859 < span class ="sa "> r</ span > < span class ="sd "> """This function is deprecated and will be removed in a future release.</ span >
@@ -881,7 +881,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
881881 < span class ="k "> return</ span > < span class ="n "> are_deterministic_algorithms_enabled</ span > < span class ="p "> ()</ span >
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884- < div class =" viewcode-block " id =" set_warn_always " > < a class =" viewcode-back " href =" ../generated/torch.set_warn_always.html#torch.set_warn_always " > [docs] </ a > < span class ="k "> def</ span > < span class ="nf "> set_warn_always</ span > < span class ="p "> (</ span > < span class ="n "> b</ span > < span class ="p "> ):</ span >
884+ < span class ="k "> def</ span > < span class ="nf "> set_warn_always</ span > < span class ="p "> (</ span > < span class ="n "> b</ span > < span class ="p "> ):</ span >
885885 < span class ="sa "> r</ span > < span class ="sd "> """When this flag is False (default) then some PyTorch warnings may only</ span >
886886< span class ="sd "> appear once per process. This helps avoid excessive warning information.</ span >
887887< span class ="sd "> Setting it to True causes these warnings to always appear, which may be</ span >
@@ -891,7 +891,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
891891< span class ="sd "> b (:class:`bool`): If True, force warnings to always be emitted</ span >
892892< span class ="sd "> If False, set to the default behaviour</ span >
893893< span class ="sd "> """</ span >
894- < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_warnAlways</ span > < span class ="p "> (</ span > < span class ="n "> b</ span > < span class ="p "> )</ span > </ div >
894+ < span class ="n "> _C</ span > < span class ="o "> .</ span > < span class ="n "> _set_warnAlways</ span > < span class ="p "> (</ span > < span class ="n "> b</ span > < span class ="p "> )</ span >
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896896< div class ="viewcode-block " id ="is_warn_always_enabled "> < a class ="viewcode-back " href ="../generated/torch.is_warn_always_enabled.html#torch.is_warn_always_enabled "> [docs]</ a > < span class ="k "> def</ span > < span class ="nf "> is_warn_always_enabled</ span > < span class ="p "> ():</ span >
897897 < span class ="sa "> r</ span > < span class ="sd "> """Returns True if the global warn_always flag is turned on. Refer to</ span >
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