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<li class="toctree-l2"><a class="reference internal" href="generated/torch.cosh.html">torch.cosh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.deg2rad.html">torch.deg2rad</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.div.html">torch.div</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.divide.html">torch.divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.digamma.html">torch.digamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erf.html">torch.erf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfc.html">torch.erfc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfinv.html">torch.erfinv</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp.html">torch.exp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp2.html">torch.exp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.expm1.html">torch.expm1</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fake_quantize_per_channel_affine.html">torch.fake_quantize_per_channel_affine</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fake_quantize_per_tensor_affine.html">torch.fake_quantize_per_tensor_affine</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fix.html">torch.fix</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.float_power.html">torch.float_power</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor.html">torch.floor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor_divide.html">torch.floor_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmod.html">torch.fmod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.frac.html">torch.frac</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.frexp.html">torch.frexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gradient.html">torch.gradient</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.imag.html">torch.imag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ldexp.html">torch.ldexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lerp.html">torch.lerp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lgamma.html">torch.lgamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log.html">torch.log</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log10.html">torch.log10</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log1p.html">torch.log1p</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log2.html">torch.log2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp.html">torch.logaddexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp2.html">torch.logaddexp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_and.html">torch.logical_and</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_not.html">torch.logical_not</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_or.html">torch.logical_or</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_xor.html">torch.logical_xor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logit.html">torch.logit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hypot.html">torch.hypot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.i0.html">torch.i0</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.igamma.html">torch.igamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.igammac.html">torch.igammac</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mul.html">torch.mul</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.multiply.html">torch.multiply</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mvlgamma.html">torch.mvlgamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nan_to_num.html">torch.nan_to_num</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.neg.html">torch.neg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.negative.html">torch.negative</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nextafter.html">torch.nextafter</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.polygamma.html">torch.polygamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.positive.html">torch.positive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.pow.html">torch.pow</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_batch_norm.html">torch.quantized_batch_norm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_max_pool1d.html">torch.quantized_max_pool1d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_max_pool2d.html">torch.quantized_max_pool2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rad2deg.html">torch.rad2deg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.real.html">torch.real</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.reciprocal.html">torch.reciprocal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.remainder.html">torch.remainder</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.round.html">torch.round</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rsqrt.html">torch.rsqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sigmoid.html">torch.sigmoid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sign.html">torch.sign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sgn.html">torch.sgn</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.signbit.html">torch.signbit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sin.html">torch.sin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinc.html">torch.sinc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinh.html">torch.sinh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.softmax.html">torch.softmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sqrt.html">torch.sqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.square.html">torch.square</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sub.html">torch.sub</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.subtract.html">torch.subtract</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tan.html">torch.tan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tanh.html">torch.tanh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.true_divide.html">torch.true_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.trunc.html">torch.trunc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xlogy.html">torch.xlogy</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmax.html">torch.argmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmin.html">torch.argmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amax.html">torch.amax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amin.html">torch.amin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.aminmax.html">torch.aminmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.all.html">torch.all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.any.html">torch.any</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.max.html">torch.max</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.min.html">torch.min</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.dist.html">torch.dist</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logsumexp.html">torch.logsumexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mean.html">torch.mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanmean.html">torch.nanmean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.median.html">torch.median</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanmedian.html">torch.nanmedian</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mode.html">torch.mode</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.norm.html">torch.norm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nansum.html">torch.nansum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.prod.html">torch.prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantile.html">torch.quantile</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanquantile.html">torch.nanquantile</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.std.html">torch.std</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.std_mean.html">torch.std_mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sum.html">torch.sum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.unique.html">torch.unique</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.unique_consecutive.html">torch.unique_consecutive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.var.html">torch.var</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.var_mean.html">torch.var_mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.count_nonzero.html">torch.count_nonzero</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.allclose.html">torch.allclose</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argsort.html">torch.argsort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.eq.html">torch.eq</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.equal.html">torch.equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ge.html">torch.ge</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater_equal.html">torch.greater_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gt.html">torch.gt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater.html">torch.greater</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isclose.html">torch.isclose</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isfinite.html">torch.isfinite</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isin.html">torch.isin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isinf.html">torch.isinf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isposinf.html">torch.isposinf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isneginf.html">torch.isneginf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isnan.html">torch.isnan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isreal.html">torch.isreal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kthvalue.html">torch.kthvalue</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.le.html">torch.le</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less_equal.html">torch.less_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lt.html">torch.lt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less.html">torch.less</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.maximum.html">torch.maximum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.minimum.html">torch.minimum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmax.html">torch.fmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmin.html">torch.fmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ne.html">torch.ne</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.not_equal.html">torch.not_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sort.html">torch.sort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.topk.html">torch.topk</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.msort.html">torch.msort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.stft.html">torch.stft</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.istft.html">torch.istft</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bartlett_window.html">torch.bartlett_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.blackman_window.html">torch.blackman_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hamming_window.html">torch.hamming_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hann_window.html">torch.hann_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kaiser_window.html">torch.kaiser_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_1d.html">torch.atleast_1d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_2d.html">torch.atleast_2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_3d.html">torch.atleast_3d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bincount.html">torch.bincount</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.block_diag.html">torch.block_diag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_tensors.html">torch.broadcast_tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_to.html">torch.broadcast_to</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_shapes.html">torch.broadcast_shapes</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bucketize.html">torch.bucketize</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cartesian_prod.html">torch.cartesian_prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cdist.html">torch.cdist</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.clone.html">torch.clone</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.combinations.html">torch.combinations</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.corrcoef.html">torch.corrcoef</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cov.html">torch.cov</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cross.html">torch.cross</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cummax.html">torch.cummax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cummin.html">torch.cummin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cumprod.html">torch.cumprod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cumsum.html">torch.cumsum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diag.html">torch.diag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diag_embed.html">torch.diag_embed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diagflat.html">torch.diagflat</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diagonal.html">torch.diagonal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diff.html">torch.diff</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.einsum.html">torch.einsum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flatten.html">torch.flatten</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flip.html">torch.flip</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fliplr.html">torch.fliplr</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flipud.html">torch.flipud</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kron.html">torch.kron</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rot90.html">torch.rot90</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gcd.html">torch.gcd</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histc.html">torch.histc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histogram.html">torch.histogram</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histogramdd.html">torch.histogramdd</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.meshgrid.html">torch.meshgrid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lcm.html">torch.lcm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logcumsumexp.html">torch.logcumsumexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ravel.html">torch.ravel</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.renorm.html">torch.renorm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.repeat_interleave.html">torch.repeat_interleave</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.roll.html">torch.roll</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.searchsorted.html">torch.searchsorted</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tensordot.html">torch.tensordot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.trace.html">torch.trace</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tril.html">torch.tril</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tril_indices.html">torch.tril_indices</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.triu.html">torch.triu</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.triu_indices.html">torch.triu_indices</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.unflatten.html">torch.unflatten</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.vander.html">torch.vander</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.view_as_real.html">torch.view_as_real</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.view_as_complex.html">torch.view_as_complex</a></li>
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</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="nn.html">torch.nn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Hardtanh.html">Hardtanh</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.LeakyReLU.html">LeakyReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.ReLU6.html">ReLU6</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.RReLU.html">RReLU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.SELU.html">SELU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.CELU.html">CELU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GELU.html">GELU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Sigmoid.html">Sigmoid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.SiLU.html">SiLU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Mish.html">Mish</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softplus.html">Softplus</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softshrink.html">Softshrink</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softsign.html">Softsign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Tanh.html">Tanh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Tanhshrink.html">Tanhshrink</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Threshold.html">Threshold</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GLU.html">GLU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmin.html">Softmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmax.html">Softmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmax2d.html">Softmax2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.LogSoftmax.html">LogSoftmax</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.RNN.html">RNN</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.LSTM.html">LSTM</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GRU.html">GRU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.RNNCell.html">RNNCell</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Bilinear.html">Bilinear</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.parametrizations.spectral_norm.html">torch.nn.utils.parametrizations.spectral_norm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.rnn.pad_packed_sequence.html">torch.nn.utils.rnn.pad_packed_sequence</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.rnn.pad_sequence.html">torch.nn.utils.rnn.pad_sequence</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.rnn.pack_sequence.html">torch.nn.utils.rnn.pack_sequence</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.rnn.unpad_sequence.html">torch.nn.utils.rnn.unpad_sequence</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Flatten.html">Flatten</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="nn.functional.html">torch.nn.functional</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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</ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.driver_allocated_memory.html">torch.mps.driver_allocated_memory</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.recommended_max_memory.html">torch.mps.recommended_max_memory</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.compile_shader.html">torch.mps.compile_shader</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.start.html">torch.mps.profiler.start</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.stop.html">torch.mps.profiler.stop</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.profile.html">torch.mps.profiler.profile</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.is_metal_capture_enabled.html">torch.mps.profiler.is_metal_capture_enabled</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.metal_capture.html">torch.mps.profiler.metal_capture</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.event.Event.html">Event</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="xpu.html">torch.xpu</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.StreamContext.html">StreamContext</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.current_device.html">torch.xpu.current_device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.current_stream.html">torch.xpu.current_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device.html">device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device_count.html">torch.xpu.device_count</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device_of.html">device_of</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_arch_list.html">torch.xpu.get_arch_list</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_capability.html">torch.xpu.get_device_capability</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_name.html">torch.xpu.get_device_name</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_properties.html">torch.xpu.get_device_properties</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_gencode_flags.html">torch.xpu.get_gencode_flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_stream_from_external.html">torch.xpu.get_stream_from_external</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.init.html">torch.xpu.init</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.is_available.html">torch.xpu.is_available</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.is_initialized.html">torch.xpu.is_initialized</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_device.html">torch.xpu.set_device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_stream.html">torch.xpu.set_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.stream.html">torch.xpu.stream</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_rng_state.html">torch.xpu.get_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_rng_state_all.html">torch.xpu.get_rng_state_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.initial_seed.html">torch.xpu.initial_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.manual_seed.html">torch.xpu.manual_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.manual_seed_all.html">torch.xpu.manual_seed_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.seed.html">torch.xpu.seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.seed_all.html">torch.xpu.seed_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_rng_state.html">torch.xpu.set_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_rng_state_all.html">torch.xpu.set_rng_state_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.Event.html">Event</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.Stream.html">Stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.empty_cache.html">torch.xpu.empty_cache</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.max_memory_allocated.html">torch.xpu.max_memory_allocated</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.max_memory_reserved.html">torch.xpu.max_memory_reserved</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.mem_get_info.html">torch.xpu.mem_get_info</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_allocated.html">torch.xpu.memory_allocated</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_reserved.html">torch.xpu.memory_reserved</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_stats.html">torch.xpu.memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_stats_as_nested_dict.html">torch.xpu.memory_stats_as_nested_dict</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.reset_accumulated_memory_stats.html">torch.xpu.reset_accumulated_memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.reset_peak_memory_stats.html">torch.xpu.reset_peak_memory_stats</a></li>
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</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="mtia.html">torch.mtia</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.StreamContext.html">StreamContext</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.current_stream.html">torch.mtia.current_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.default_stream.html">torch.mtia.default_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.device_count.html">torch.mtia.device_count</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.init.html">torch.mtia.init</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.is_available.html">torch.mtia.is_available</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.is_initialized.html">torch.mtia.is_initialized</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.memory_stats.html">torch.mtia.memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.get_device_capability.html">torch.mtia.get_device_capability</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.empty_cache.html">torch.mtia.empty_cache</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.record_memory_history.html">torch.mtia.record_memory_history</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.snapshot.html">torch.mtia.snapshot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.attach_out_of_memory_observer.html">torch.mtia.attach_out_of_memory_observer</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_device.html">torch.mtia.set_device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_stream.html">torch.mtia.set_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.stream.html">torch.mtia.stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.synchronize.html">torch.mtia.synchronize</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.device.html">device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_rng_state.html">torch.mtia.set_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.get_rng_state.html">torch.mtia.get_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.DeferredMtiaCallError.html">torch.mtia.DeferredMtiaCallError</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.Event.html">Event</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.Stream.html">Stream</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="mtia.memory.html">torch.mtia.memory</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.memory.memory_stats.html">torch.mtia.memory.memory_stats</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="meta.html">Meta device</a></li>
<li class="toctree-l1"><a class="reference internal" href="backends.html">torch.backends</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="export.html">torch.export</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="export.programming_model.html">torch.export Programming Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="export.ir_spec.html">torch.export IR Specification</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler_ir.html">IRs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/torch.cond.html">torch.cond</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/torch.dynamic-shape.html">torch.dynamic-shape</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/python.closure.html">python.closure</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/python.data-structure.html">python.data-structure</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/torch.mutation.html">torch.mutation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="cond.html">Control Flow - Cond</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler_fake_tensor.html">Fake tensor</a></li>
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<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.tensor.html">torch.distributed.tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.algorithms.join.html">torch.distributed.algorithms.join</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="distributed.elastic.html">torch.distributed.elastic</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="elastic/quickstart.html">Quickstart</a></li>
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<li class="toctree-l2"><a class="reference internal" href="elastic/kubernetes.html">TorchElastic Kubernetes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="fsdp.html">torch.distributed.fsdp</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler_get_started.html">Getting Started</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler.config.html">torch.compiler.config</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_fine_grain_apis.html">TorchDynamo APIs for fine-grained tracing</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="torch.compiler_aot_inductor.html">AOTInductor: Ahead-Of-Time Compilation for Torch.Export-ed Models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l3"><a class="reference internal" href="torch.compiler_aot_inductor_minifier.html">AOTInductor Minifier</a></li>
</ul>
</details></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_inductor_profiling.html">TorchInductor GPU Profiling</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_profiling_torch_compile.html">Profiling to understand torch.compile performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_troubleshooting.html">torch.compile Troubleshooting</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_performance_dashboard.html">PyTorch 2.0 Performance Dashboard</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamo_overview.html">Dynamo Overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamo_deepdive.html">Dynamo Deep-Dive</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamic_shapes.html">Dynamic shapes</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_nn_module.html">PyTorch 2.0 NNModule Support</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_best_practices_for_backends.html">Best Practices for Backends</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_cudagraph_trees.html">CUDAGraph Trees</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="onnx_dynamo.html">TorchDynamo-based ONNX Exporter</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="quantization-support.html">Quantization API Reference</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.quantize.html">quantize</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerTensor.html">PerTensor</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerToken.html">PerToken</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.TorchAODType.html">TorchAODType</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.get_block_size.html">get_block_size</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FakeQuantize.html">FakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html">FixedQParamsFakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html">FusedMovingAvgObsFakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fake_quant.html">default_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html">default_weight_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html">default_per_channel_weight_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html">default_histogram_fake_quant</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html">default_fused_wt_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html">default_fused_per_channel_wt_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html">disable_fake_quant</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.QConfig.html">QConfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qconfig.html">default_qconfig</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html">default_per_channel_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html">default_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html">float16_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float16_static_qconfig.html">float16_static_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html">per_channel_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html">float_qparams_weight_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qat_qconfig.html">default_qat_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html">default_weight_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html">default_activation_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html">default_qat_qconfig_v2</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU1d.html">ConvReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn1d.html">ConvBn1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn2d.html">ConvBn2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn3d.html">ConvBn3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html">ConvBnReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html">ConvBnReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html">ConvBnReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU2d.html">BNReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU3d.html">BNReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html">ConvBn1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html">ConvBnReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html">ConvBn2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html">ConvBnReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html">ConvBn3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html">ConvBnReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html">update_bn_stats</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html">freeze_bn_stats</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html">BNReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html">BNReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html">ConvReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Conv2d.html">Conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Conv3d.html">Conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.dynamic.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ReLU6.html">ReLU6</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Hardswish.html">Hardswish</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ELU.html">ELU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.LeakyReLU.html">LeakyReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Sigmoid.html">Sigmoid</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.BatchNorm2d.html">BatchNorm2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.BatchNorm3d.html">BatchNorm3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv1d.html">Conv1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv2d.html">Conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv3d.html">Conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose1d.html">ConvTranspose1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose2d.html">ConvTranspose2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose3d.html">ConvTranspose3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Embedding.html">Embedding</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.EmbeddingBag.html">EmbeddingBag</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.FloatFunctional.html">FloatFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.FXFloatFunctional.html">FXFloatFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.QFunctional.html">QFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.LayerNorm.html">LayerNorm</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.GroupNorm.html">GroupNorm</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm1d.html">InstanceNorm1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm2d.html">InstanceNorm2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm3d.html">InstanceNorm3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.avg_pool2d.html">avg_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.avg_pool3d.html">avg_pool3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html">adaptive_avg_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html">adaptive_avg_pool3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv1d.html">conv1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv2d.html">conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv3d.html">conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.interpolate.html">interpolate</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.linear.html">linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.max_pool1d.html">max_pool1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.max_pool2d.html">max_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.celu.html">celu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.leaky_relu.html">leaky_relu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardtanh.html">hardtanh</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardswish.html">hardswish</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.threshold.html">threshold</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.elu.html">elu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardsigmoid.html">hardsigmoid</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.clamp.html">clamp</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample.html">upsample</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample_bilinear.html">upsample_bilinear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample_nearest.html">upsample_nearest</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantizable.LSTM.html">LSTM</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantizable.MultiheadAttention.html">MultiheadAttention</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.LSTM.html">LSTM</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.GRU.html">GRU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.RNNCell.html">RNNCell</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.LSTMCell.html">LSTMCell</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.GRUCell.html">GRUCell</a></li>
</ul>
</details></li>
<li class="toctree-l2"><a class="reference internal" href="quantization-backend-configuration.html">Quantization Backend Configuration</a></li>
<li class="toctree-l2"><a class="reference internal" href="quantization-accuracy-debugging.html">Quantization Accuracy Debugging</a></li>

</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="rpc.html">Distributed RPC Framework</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="rpc/rref.html">Remote Reference Protocol</a></li>
<li class="toctree-l2"><a class="reference internal" href="rpc/distributed_autograd.html">Distributed Autograd Design</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="random.html">torch.random</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.absolute.html">torch.absolute</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.acos.html">torch.acos</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arccos.html">torch.arccos</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.acosh.html">torch.acosh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arccosh.html">torch.arccosh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.angle.html">torch.angle</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.asin.html">torch.asin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arcsin.html">torch.arcsin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.asinh.html">torch.asinh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arcsinh.html">torch.arcsinh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atan.html">torch.atan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arctan.html">torch.arctan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atanh.html">torch.atanh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arctanh.html">torch.arctanh</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.ceil.html">torch.ceil</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.clamp.html">torch.clamp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.clip.html">torch.clip</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.conj_physical.html">torch.conj_physical</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cos.html">torch.cos</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cosh.html">torch.cosh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.deg2rad.html">torch.deg2rad</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.digamma.html">torch.digamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erf.html">torch.erf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfc.html">torch.erfc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfinv.html">torch.erfinv</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp.html">torch.exp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp2.html">torch.exp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.expm1.html">torch.expm1</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fix.html">torch.fix</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor.html">torch.floor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.frac.html">torch.frac</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lgamma.html">torch.lgamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log.html">torch.log</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log10.html">torch.log10</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log1p.html">torch.log1p</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log2.html">torch.log2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logit.html">torch.logit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.i0.html">torch.i0</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isnan.html">torch.isnan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nan_to_num.html">torch.nan_to_num</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.neg.html">torch.neg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.negative.html">torch.negative</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.positive.html">torch.positive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.pow.html">torch.pow</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rad2deg.html">torch.rad2deg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.reciprocal.html">torch.reciprocal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.round.html">torch.round</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rsqrt.html">torch.rsqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sigmoid.html">torch.sigmoid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sign.html">torch.sign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sgn.html">torch.sgn</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.signbit.html">torch.signbit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sin.html">torch.sin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinc.html">torch.sinc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinh.html">torch.sinh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sqrt.html">torch.sqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.square.html">torch.square</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tan.html">torch.tan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tanh.html">torch.tanh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.trunc.html">torch.trunc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.angle.html">torch.angle</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.positive.html">torch.positive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.signbit.html">torch.signbit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isnan.html">torch.isnan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.add.html">torch.add</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atan2.html">torch.atan2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.arctan2.html">torch.arctan2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bitwise_and.html">torch.bitwise_and</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bitwise_or.html">torch.bitwise_or</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bitwise_xor.html">torch.bitwise_xor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bitwise_left_shift.html">torch.bitwise_left_shift</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bitwise_right_shift.html">torch.bitwise_right_shift</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.div.html">torch.div</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.divide.html">torch.divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor_divide.html">torch.floor_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmod.html">torch.fmod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp.html">torch.logaddexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp2.html">torch.logaddexp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mul.html">torch.mul</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.multiply.html">torch.multiply</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nextafter.html">torch.nextafter</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.remainder.html">torch.remainder</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sub.html">torch.sub</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.subtract.html">torch.subtract</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.true_divide.html">torch.true_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.eq.html">torch.eq</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ne.html">torch.ne</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.le.html">torch.le</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ge.html">torch.ge</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater.html">torch.greater</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater_equal.html">torch.greater_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gt.html">torch.gt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less_equal.html">torch.less_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lt.html">torch.lt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less.html">torch.less</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.maximum.html">torch.maximum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.minimum.html">torch.minimum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmax.html">torch.fmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmin.html">torch.fmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.not_equal.html">torch.not_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp.html">torch.logaddexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp2.html">torch.logaddexp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.equal.html">torch.equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmin.html">torch.fmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.minimum.html">torch.minimum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmax.html">torch.fmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sum.html">torch.sum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mean.html">torch.mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amin.html">torch.amin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amax.html">torch.amax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmin.html">torch.argmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmax.html">torch.argmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.prod.html">torch.prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.all.html">torch.all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.norm.html">torch.norm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.var.html">torch.var</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.std.html">torch.std</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_1d.html">torch.atleast_1d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_tensors.html">torch.broadcast_tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_to.html">torch.broadcast_to</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cat.html">torch.cat</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.chunk.html">torch.chunk</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.column_stack.html">torch.column_stack</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.dsplit.html">torch.dsplit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flatten.html">torch.flatten</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hsplit.html">torch.hsplit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hstack.html">torch.hstack</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kron.html">torch.kron</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.meshgrid.html">torch.meshgrid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.narrow.html">torch.narrow</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.functional.unfold.html">torch.nn.functional.unfold</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ravel.html">torch.ravel</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.select.html">torch.select</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.split.html">torch.split</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.stack.html">torch.stack</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.t.html">torch.t</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.transpose.html">torch.transpose</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.vsplit.html">torch.vsplit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.vstack.html">torch.vstack</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.expand.html">torch.Tensor.expand</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.expand_as.html">torch.Tensor.expand_as</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.reshape.html">torch.Tensor.reshape</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.reshape_as.html">torch.Tensor.reshape_as</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.unfold.html">torch.Tensor.unfold</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.view.html">torch.Tensor.view</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="nested.html">torch.nested</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="sparse.html">torch.sparse</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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  <div class="section" id="torch-nn-init">
<span id="nn-init-doc"></span><h1>torch.nn.init<a class="headerlink" href="#torch-nn-init" title="Permalink to this heading">#</a></h1>
<p class="date-info-last-verified" style="color: #6c6c6d; font-size: small;">Created On: Jun 11, 2019 | Last Updated: Jul 07, 2022</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>All the functions in this module are intended to be used to initialize neural network
parameters, so they all run in <a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code></a> mode and will not be taken into
account by autograd.</p>
</div>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.calculate_gain">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">#</a></dt>
<dd><p>Return the recommended gain value for the given nonlinearity function.</p>
<p>The values are as follows:</p>
<div class="pst-scrollable-table-container"><table class="table">
<colgroup>
<col style="width: 24%" />
<col style="width: 76%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>nonlinearity</p></th>
<th class="head"><p>gain</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Linear / Identity</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn></mrow><annotation encoding="application/x-tex">1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p>Conv{1,2,3}D</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn></mrow><annotation encoding="application/x-tex">1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p>Sigmoid</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn></mrow><annotation encoding="application/x-tex">1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p>Tanh</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfrac><mn>5</mn><mn>3</mn></mfrac></mrow><annotation encoding="application/x-tex">\frac{5}{3}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1901em;vertical-align:-0.345em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">3</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">5</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.345em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p>ReLU</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><mn>2</mn></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{2}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.04em;vertical-align:-0.1328em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9072em;"><span class="svg-align" style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord" style="padding-left:0.833em;"><span class="mord">2</span></span></span><span style="top:-2.8672em;"><span class="pstrut" style="height:3em;"></span><span class="hide-tail" style="min-width:0.853em;height:1.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.08em" viewBox="0 0 400000 1080" preserveAspectRatio="xMinYMin slice"><path d="M95,702
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c69,-144,104.5,-217.7,106.5,-221
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M834 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.1328em;"><span></span></span></span></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p>Leaky Relu</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><mfrac><mn>2</mn><mrow><mn>1</mn><mo>+</mo><msup><mtext>negative_slope</mtext><mn>2</mn></msup></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.84em;vertical-align:-0.7266em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1134em;"><span class="svg-align" style="top:-3.8em;"><span class="pstrut" style="height:3.8em;"></span><span class="mord" style="padding-left:1em;"><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.6287em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">negative_slope</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8019em;"><span style="top:-2.8416em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5883em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.0734em;"><span class="pstrut" style="height:3.8em;"></span><span class="hide-tail" style="min-width:1.02em;height:1.88em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.88em" viewBox="0 0 400000 1944" preserveAspectRatio="xMinYMin slice"><path d="M983 90
l0 -0
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c53.7,-170.3,84.5,-266.8,92.5,-289.5z
M1001 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.7266em;"><span></span></span></span></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p>SELU</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfrac><mn>3</mn><mn>4</mn></mfrac></mrow><annotation encoding="application/x-tex">\frac{3}{4}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1901em;vertical-align:-0.345em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">4</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">3</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.345em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p></td>
</tr>
</tbody>
</table>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>In order to implement <a class="reference external" href="https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html">Self-Normalizing Neural Networks</a> ,
you should use <code class="docutils literal notranslate"><span class="pre">nonlinearity='linear'</span></code> instead of <code class="docutils literal notranslate"><span class="pre">nonlinearity='selu'</span></code>.
This gives the initial weights a variance of <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">N</span></code>,
which is necessary to induce a stable fixed point in the forward pass.
In contrast, the default gain for <code class="docutils literal notranslate"><span class="pre">SELU</span></code> sacrifices the normalization
effect for more stable gradient flow in rectangular layers.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nonlinearity</strong> – the non-linear function (<cite>nn.functional</cite> name)</p></li>
<li><p><strong>param</strong> – optional parameter for the non-linear function</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">gain</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">calculate_gain</span><span class="p">(</span><span class="s1">&#39;leaky_relu&#39;</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span>  <span class="c1"># leaky_relu with negative_slope=0.2</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with values drawn from the uniform distribution.</p>
<p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(a, b)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the lower bound of the uniform distribution</p></li>
<li><p><strong>b</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the upper bound of the uniform distribution</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with values drawn from the normal distribution.</p>
<p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the mean of the normal distribution</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the standard deviation of the normal distribution</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.constant_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with the value <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>val</mtext></mrow><annotation encoding="application/x-tex">\text{val}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">val</span></span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>val</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the value to fill the tensor with</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.ones_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with the scalar value <cite>1</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">ones_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.zeros_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with the scalar value <cite>0</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.eye_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the 2-dimensional input <cite>Tensor</cite> with the identity matrix.</p>
<p>Preserves the identity of the inputs in <cite>Linear</cite> layers, where as
many inputs are preserved as possible.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> – a 2-dimensional <cite>torch.Tensor</cite></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">eye_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.dirac_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the {3, 4, 5}-dimensional input <cite>Tensor</cite> with the Dirac delta function.</p>
<p>Preserves the identity of the inputs in <cite>Convolutional</cite>
layers, where as many input channels are preserved as possible. In case
of groups&gt;1, each group of channels preserves identity</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> – a {3, 4, 5}-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>optional</em>) – number of groups in the conv layer (default: 1)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">dirac_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">dirac_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.xavier_uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier uniform distribution.</p>
<p>The method is described in <cite>Understanding the difficulty of training
deep feedforward neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mo>−</mo><mi>a</mi><mo separator="true">,</mo><mi>a</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(-a, a)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord">−</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">a</span><span class="mclose">)</span></span></span></span></span> where</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>a</mi><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>6</mn><mrow><mtext>fan_in</mtext><mo>+</mo><mtext>fan_out</mtext></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}

</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_in</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord text"><span class="mord">fan_out</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">6</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
c339.3,-1799.3,509.3,-2700,510,-2702 l0 -0
c3.3,-7.3,9.3,-11,18,-11 H400000v40H1017.7
s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
c-8,0,-12,-0.7,-12,-2c0,-1.3,-5.3,-32,-16,-92c-50.7,-293.3,-119.7,-693.3,-207,-1200
c0,-1.3,-5.3,8.7,-16,30c-10.7,21.3,-21.3,42.7,-32,64s-16,33,-16,33s-26,-26,-26,-26
s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as Glorot initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – an optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">calculate_gain</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.xavier_uniform_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.xavier_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier normal distribution.</p>
<p>The method is described in <cite>Understanding the difficulty of training deep feedforward
neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010). The resulting tensor
will have values sampled from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> where</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>std</mtext><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>2</mn><mrow><mtext>fan_in</mtext><mo>+</mo><mtext>fan_out</mtext></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}

</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">std</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_in</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord text"><span class="mord">fan_out</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
c339.3,-1799.3,509.3,-2700,510,-2702 l0 -0
c3.3,-7.3,9.3,-11,18,-11 H400000v40H1017.7
s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
c-8,0,-12,-0.7,-12,-2c0,-1.3,-5.3,-32,-16,-92c-50.7,-293.3,-119.7,-693.3,-207,-1200
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s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as Glorot initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – an optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.xavier_normal_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.kaiming_uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming uniform distribution.</p>
<p>The method is described in <cite>Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mo>−</mo><mtext>bound</mtext><mo separator="true">,</mo><mtext>bound</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(-\text{bound}, \text{bound})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord">−</span><span class="mord text"><span class="mord">bound</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord text"><span class="mord">bound</span></span><span class="mclose">)</span></span></span></span></span> where</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>bound</mtext><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>3</mn><mtext>fan_mode</mtext></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}

</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">bound</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_mode</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">3</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
c339.3,-1799.3,509.3,-2700,510,-2702 l0 -0
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s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
c-8,0,-12,-0.7,-12,-2c0,-1.3,-5.3,-32,-16,-92c-50.7,-293.3,-119.7,-693.3,-207,-1200
c0,-1.3,-5.3,8.7,-16,30c-10.7,21.3,-21.3,42.7,-32,64s-16,33,-16,33s-26,-26,-26,-26
s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as He initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the negative slope of the rectifier used after this layer (only
used with <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code>)</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – either <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code> (default) or <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code>. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code>
preserves the magnitude of the variance of the weights in the
forward pass. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code> preserves the magnitudes in the
backwards pass.</p></li>
<li><p><strong>nonlinearity</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – the non-linear function (<cite>nn.functional</cite> name),
recommended to use only with <code class="docutils literal notranslate"><span class="pre">'relu'</span></code> or <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code> (default).</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_in&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.kaiming_uniform_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.kaiming_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming normal distribution.</p>
<p>The method is described in <cite>Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> where</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>std</mtext><mo>=</mo><mfrac><mtext>gain</mtext><msqrt><mtext>fan_mode</mtext></msqrt></mfrac></mrow><annotation encoding="application/x-tex">\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}

</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">std</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:2.4749em;vertical-align:-1.13em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3449em;"><span style="top:-2.2328em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8772em;"><span class="svg-align" style="top:-3.2em;"><span class="pstrut" style="height:3.2em;"></span><span class="mord" style="padding-left:1em;"><span class="mord text"><span class="mord">fan_mode</span></span></span></span><span style="top:-2.8372em;"><span class="pstrut" style="height:3.2em;"></span><span class="hide-tail" style="min-width:1.02em;height:1.28em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.28em" viewBox="0 0 400000 1296" preserveAspectRatio="xMinYMin slice"><path d="M263,681c0.7,0,18,39.7,52,119
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M1001 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.3628em;"><span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">gain</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.13em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></div><p>Also known as He initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the negative slope of the rectifier used after this layer (only
used with <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code>)</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – either <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code> (default) or <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code>. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code>
preserves the magnitude of the variance of the weights in the
forward pass. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code> preserves the magnitudes in the
backwards pass.</p></li>
<li><p><strong>nonlinearity</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – the non-linear function (<cite>nn.functional</cite> name),
recommended to use only with <code class="docutils literal notranslate"><span class="pre">'relu'</span></code> or <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code> (default).</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_out&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">&#64;</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.kaiming_normal_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.trunc_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input Tensor with values drawn from a truncated normal distribution.</p>
<p>The values are effectively drawn from the
normal distribution <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>
with values outside <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">[</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">[a, b]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">[</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">]</span></span></span></span></span> redrawn until they are within
the bounds. The method used for generating the random values works
best when <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi><mo>≤</mo><mtext>mean</mtext><mo>≤</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">a \leq \text{mean} \leq b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord text"><span class="mord">mean</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the mean of the normal distribution</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the standard deviation of the normal distribution</p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the minimum cutoff value</p></li>
<li><p><strong>b</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the maximum cutoff value</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">trunc_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.orthogonal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with a (semi) orthogonal matrix.</p>
<p>Described in <cite>Exact solutions to the nonlinear dynamics of learning in deep
linear neural networks</cite> - Saxe, A. et al. (2013). The input tensor must have
at least 2 dimensions, and for tensors with more than 2 dimensions the
trailing dimensions are flattened.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> – an n-dimensional <cite>torch.Tensor</cite>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>≥</mo><mn>2</mn></mrow><annotation encoding="application/x-tex">n \geq 2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord mathnormal">n</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≥</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">2</span></span></span></span></span></p></li>
<li><p><strong>gain</strong> – optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">orthogonal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.sparse_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">sparse_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/nn/init.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">#</a></dt>
<dd><p>Fill the 2D input <cite>Tensor</cite> as a sparse matrix.</p>
<p>The non-zero elements will be drawn from the normal distribution
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mn>0.01</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, 0.01)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">0.01</span><span class="mclose">)</span></span></span></span></span>, as described in <cite>Deep learning via
Hessian-free optimization</cite> - Martens, J. (2010).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>sparsity</strong> – The fraction of elements in each column to be set to zero</p></li>
<li><p><strong>std</strong> – the standard deviation of the normal distribution used to generate
the non-zero values</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">sparse_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">sparsity</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>

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