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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<li><a href="generated/torch.abs.html#torch.abs">abs() (in module torch)</a>
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<li><a href="named_tensor.html#torch.Tensor.align_as">align_as() (torch.Tensor method)</a>
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<li><a href="generated/torch.all.html#torch.all">all() (in module torch)</a>, <a href="generated/torch.all.html#torch.all">[1]</a>
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<li><a href="distributed.html#torch.distributed.all_gather">all_gather() (in module torch.distributed)</a>
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<li><a href="distributed.html#torch.distributed.all_reduce">all_reduce() (in module torch.distributed)</a>
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<li><a href="generated/torch.angle.html#torch.angle">angle() (in module torch)</a>
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<li><a href="generated/torch.any.html#torch.any">any() (in module torch)</a>, <a href="generated/torch.any.html#torch.any">[1]</a>
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<li><a href="fx.html#torch.fx.Node.append">append() (torch.fx.Node method)</a>
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<li><a href="generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer.apply">(torch.nn.utils.prune.PruningContainer class method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.RandomStructured.html#torch.nn.utils.prune.RandomStructured.apply">(torch.nn.utils.prune.RandomStructured class method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.RandomUnstructured.html#torch.nn.utils.prune.RandomUnstructured.apply">(torch.nn.utils.prune.RandomUnstructured class method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="tensors.html#torch.Tensor.apply_">apply_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.BasePruningMethod.html#torch.nn.utils.prune.BasePruningMethod.apply_mask">apply_mask() (torch.nn.utils.prune.BasePruningMethod method)</a>
<ul>
<li><a href="generated/torch.nn.utils.prune.CustomFromMask.html#torch.nn.utils.prune.CustomFromMask.apply_mask">(torch.nn.utils.prune.CustomFromMask method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.Identity.html#torch.nn.utils.prune.Identity.apply_mask">(torch.nn.utils.prune.Identity method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.L1Unstructured.html#torch.nn.utils.prune.L1Unstructured.apply_mask">(torch.nn.utils.prune.L1Unstructured method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.LnStructured.html#torch.nn.utils.prune.LnStructured.apply_mask">(torch.nn.utils.prune.LnStructured method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer.apply_mask">(torch.nn.utils.prune.PruningContainer method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.RandomStructured.html#torch.nn.utils.prune.RandomStructured.apply_mask">(torch.nn.utils.prune.RandomStructured method)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.RandomUnstructured.html#torch.nn.utils.prune.RandomUnstructured.apply_mask">(torch.nn.utils.prune.RandomUnstructured method)</a>
</li>
</ul></li>
<li><a href="generated/torch.arange.html#torch.arange">arange() (in module torch)</a>
</li>
<li><a href="generated/torch.arccos.html#torch.arccos">arccos() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arccos">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arccos_">arccos_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.arccosh.html#torch.arccosh">arccosh() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arccosh">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arccosh_">arccosh_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.arcsin.html#torch.arcsin">arcsin() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arcsin">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arcsin_">arcsin_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.arcsinh.html#torch.arcsinh">arcsinh() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arcsinh">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arcsinh_">arcsinh_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.arctan.html#torch.arctan">arctan() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arctan">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arctan_">arctan_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.arctanh.html#torch.arctanh">arctanh() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.arctanh">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.arctanh_">arctanh_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled">are_deterministic_algorithms_enabled() (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.continuous_bernoulli.ContinuousBernoulli.arg_constraints">(torch.distributions.continuous_bernoulli.ContinuousBernoulli 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.kumaraswamy.Kumaraswamy.arg_constraints">(torch.distributions.kumaraswamy.Kumaraswamy 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.lkj_cholesky.LKJCholesky.arg_constraints">(torch.distributions.lkj_cholesky.LKJCholesky 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.mixture_same_family.MixtureSameFamily.arg_constraints">(torch.distributions.mixture_same_family.MixtureSameFamily 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.von_mises.VonMises.arg_constraints">(torch.distributions.von_mises.VonMises 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="generated/torch.argmax.html#torch.argmax">argmax() (in module torch)</a>, <a href="generated/torch.argmax.html#torch.argmax">[1]</a>
<ul>
<li><a href="tensors.html#torch.Tensor.argmax">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="generated/torch.argmin.html#torch.argmin">argmin() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.argmin">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="fx.html#torch.fx.Node.args">args() (torch.fx.Node property)</a>
</li>
<li><a href="generated/torch.argsort.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="benchmark_utils.html#torch.utils.benchmark.CallgrindStats.as_standardized">as_standardized() (torch.utils.benchmark.CallgrindStats method)</a>
</li>
<li><a href="generated/torch.as_strided.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="tensors.html#torch.Tensor.as_subclass">as_subclass() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.as_tensor.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="generated/torch.asin.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="generated/torch.asinh.html#torch.asinh">asinh() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.asinh">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.asinh_">asinh_() (torch.Tensor method)</a>
</li>
<li><a href="rpc.html#torch.distributed.rpc.functions.async_execution">async_execution() (in module torch.distributed.rpc.functions)</a>
</li>
<li><a href="generated/torch.atan.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="generated/torch.atan2.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="generated/torch.atanh.html#torch.atanh">atanh() (in module torch)</a>
<ul>
<li><a href="tensors.html#torch.Tensor.atanh">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="tensors.html#torch.Tensor.atanh_">atanh_() (torch.Tensor method)</a>
</li>
<li><a href="generated/torch.atleast_1d.html#torch.atleast_1d">atleast_1d() (in module torch)</a>
</li>
<li><a href="generated/torch.atleast_2d.html#torch.atleast_2d">atleast_2d() (in module torch)</a>
</li>
<li><a href="generated/torch.atleast_3d.html#torch.atleast_3d">atleast_3d() (in module torch)</a>
</li>
<li><a href="amp.html#torch.cuda.amp.autocast">autocast (class in torch.cuda.amp)</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="torch.nn.quantized.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="generated/torch.nn.AvgPool1d.html#torch.nn.AvgPool1d">AvgPool1d (class in torch.nn)</a>
</li>
<li><a href="generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d">AvgPool2d (class in torch.nn)</a>
</li>
<li><a href="generated/torch.nn.AvgPool3d.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="rpc.html#torch.distributed.rpc.BackendType">BackendType (class in torch.distributed.rpc)</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="rpc.html#torch.distributed.rpc.RRef.backward">(torch.distributed.rpc.RRef method)</a>
</li>
<li><a href="autograd.html#torch.Tensor.backward">(torch.Tensor method)</a>
</li>
</ul></li>
<li><a href="generated/torch.baddbmm.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="generated/torch.bartlett_window.html#torch.bartlett_window">bartlett_window() (in module torch)</a>
</li>
<li><a href="generated/torch.nn.utils.prune.BasePruningMethod.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>