Latest development documentation · Updated 2026-09-08. A documentation snapshot for package 1.0.0.dev20260909 is not available.

tensorplay.nn.functional

Convolution functions

`tensorplay.nn.functional.conv1d`	Applies a 1D convolution over an input signal composed of several input planes.
`tensorplay.nn.functional.conv2d`	Applies a 2D convolution over an input image composed of several input planes.
`tensorplay.nn.functional.conv3d`	Applies a 3D convolution over an input image composed of several input planes.
`tensorplay.nn.functional.conv_transpose1d`
`tensorplay.nn.functional.conv_transpose2d`
`tensorplay.nn.functional.conv_transpose3d`
`tensorplay.nn.functional.unfold`
`tensorplay.nn.functional.fold`	Combine an array of sliding local blocks into a tensor containing

Pooling functions

`tensorplay.nn.functional.avg_pool1d`	avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
`tensorplay.nn.functional.avg_pool2d`
`tensorplay.nn.functional.avg_pool3d`	avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor
`tensorplay.nn.functional.max_pool1d`	max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) -> Tensor
`tensorplay.nn.functional.max_pool2d`
`tensorplay.nn.functional.max_pool3d`	max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) -> Tensor
`tensorplay.nn.functional.max_unpool1d`	Compute a partial inverse of `MaxPool1d`.
`tensorplay.nn.functional.max_unpool2d`	Compute a partial inverse of `MaxPool2d`.
`tensorplay.nn.functional.max_unpool3d`	Compute a partial inverse of `MaxPool3d`.
`tensorplay.nn.functional.lp_pool1d`	Apply a 1D power-average pooling over an input signal.
`tensorplay.nn.functional.lp_pool2d`	Apply a 2D power-average pooling over an input signal.
`tensorplay.nn.functional.lp_pool3d`	Apply a 3D power-average pooling over an input signal.
`tensorplay.nn.functional.adaptive_max_pool1d`
`tensorplay.nn.functional.adaptive_max_pool2d`
`tensorplay.nn.functional.adaptive_max_pool3d`	Applies a 3D adaptive max pooling over an input signal composed of several input planes.
`tensorplay.nn.functional.adaptive_avg_pool1d`
`tensorplay.nn.functional.adaptive_avg_pool2d`
`tensorplay.nn.functional.adaptive_avg_pool3d`	Apply a 3D adaptive average pooling over an input signal.
`tensorplay.nn.functional.fractional_max_pool2d`	Applies 2D fractional max pooling over an input signal.
`tensorplay.nn.functional.fractional_max_pool3d`	Applies 3D fractional max pooling over an input signal.

Attention Mechanisms

The tensorplay.nn.attention.bias module contains attention_biases that are designed to be used with scaled_dot_product_attention.

tensorplay.nn.functional.scaled_dot_product_attention

scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, backend=None) -> Tensor

Non-linear activation functions

`tensorplay.nn.functional.threshold`	Apply a threshold to each element of the input Tensor.
`tensorplay.nn.functional.threshold_`
`tensorplay.nn.functional.relu`
`tensorplay.nn.functional.relu_`
`tensorplay.nn.functional.hardtanh`
`tensorplay.nn.functional.hardtanh_`
`tensorplay.nn.functional.hardswish`
`tensorplay.nn.functional.relu6`
`tensorplay.nn.functional.elu`
`tensorplay.nn.functional.elu_`
`tensorplay.nn.functional.selu`
`tensorplay.nn.functional.celu`
`tensorplay.nn.functional.leaky_relu`
`tensorplay.nn.functional.leaky_relu_`
`tensorplay.nn.functional.prelu`
`tensorplay.nn.functional.rrelu`	Randomized leaky ReLU.
`tensorplay.nn.functional.rrelu_`	In-place version of `rrelu()`.
`tensorplay.nn.functional.glu`	Gated Linear Unit: $a * \sigma(b)$ where the input is split in half along `dim`.
`tensorplay.nn.functional.gelu`	When approximate is 'none', applies $\text{GELU}(x) = x * \Phi(x)$ ; 'tanh' uses the tanh estimation.
`tensorplay.nn.functional.logsigmoid`	Applies element-wise $\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)$
`tensorplay.nn.functional.hardshrink`	Applies the hard shrinkage function element-wise.
`tensorplay.nn.functional.tanhshrink`	Applies element-wise, $\text{Tanhshrink}(x) = x - \text{Tanh}(x)$
`tensorplay.nn.functional.softsign`	Applies element-wise, the function $\text{SoftSign}(x) = \frac{x}{1 + \|x\|}$
`tensorplay.nn.functional.softplus`
`tensorplay.nn.functional.softmin`	Apply a softmin function.
`tensorplay.nn.functional.softmax`
`tensorplay.nn.functional.softshrink`	Applies the soft shrinkage function element-wise.
`tensorplay.nn.functional.gumbel_softmax`	Sample from the Gumbel-Softmax distribution and optionally discretize.
`tensorplay.nn.functional.log_softmax`
`tensorplay.nn.functional.tanh`	Applies element-wise $\text{Tanh}(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}$
`tensorplay.nn.functional.sigmoid`	Applies the element-wise function $\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}$
`tensorplay.nn.functional.hardsigmoid`
`tensorplay.nn.functional.silu`	Apply the Sigmoid Linear Unit (SiLU) function, element-wise.
`tensorplay.nn.functional.mish`
`tensorplay.nn.functional.batch_norm`
`tensorplay.nn.functional.group_norm`
`tensorplay.nn.functional.instance_norm`
`tensorplay.nn.functional.layer_norm`
`tensorplay.nn.functional.local_response_norm`	Apply local response normalization over an input signal.
`tensorplay.nn.functional.rms_norm`	Apply Root Mean Square Layer Normalization.
`tensorplay.nn.functional.normalize`	Performs $L_p$ normalization over the specified dimension —

Linear functions

`tensorplay.nn.functional.linear`	Applies a linear transformation to the incoming data: $y = xA^T + b$ .
`tensorplay.nn.functional.bilinear`

Dropout functions

`tensorplay.nn.functional.dropout`
`tensorplay.nn.functional.alpha_dropout`
`tensorplay.nn.functional.feature_alpha_dropout`	Randomly masks out entire channels, setting activations to the negative saturation value of the SELU activation function.
`tensorplay.nn.functional.dropout1d`	Randomly zero out entire channels (a channel is a 1D feature map).
`tensorplay.nn.functional.dropout2d`
`tensorplay.nn.functional.dropout3d`

Sparse functions

`tensorplay.nn.functional.embedding`
`tensorplay.nn.functional.embedding_bag`	Compute sums, means or maxes of `bags` of embeddings.
`tensorplay.nn.functional.one_hot`	Returns long tensor shaped `tensor.shape + (num_classes,)` with a 1 at

Distance functions

`tensorplay.nn.functional.pairwise_distance`	Computes the pairwise distance between input vectors.
`tensorplay.nn.functional.cosine_similarity`	Returns cosine similarity between x1 and x2, computed along dim.
`tensorplay.nn.functional.pdist`	Computes the pairwise distance between rows of `input`.

Loss functions

`tensorplay.nn.functional.binary_cross_entropy`	Compute Binary Cross Entropy between the target and input probabilities.
`tensorplay.nn.functional.binary_cross_entropy_with_logits`	Compute Binary Cross Entropy between target and input logits.
`tensorplay.nn.functional.poisson_nll_loss`	Compute the Poisson negative log likelihood loss.
`tensorplay.nn.functional.cosine_embedding_loss`	Compute the cosine embedding loss.
`tensorplay.nn.functional.cross_entropy`	Compute the cross entropy loss between input logits and target.
`tensorplay.nn.functional.ctc_loss`	Compute the Connectionist Temporal Classification loss.
`tensorplay.nn.functional.gaussian_nll_loss`	Compute the Gaussian negative log likelihood loss.
`tensorplay.nn.functional.hinge_embedding_loss`	Compute the hinge embedding loss.
`tensorplay.nn.functional.kl_div`	Compute the KL Divergence loss.
`tensorplay.nn.functional.l1_loss`	Compute the L1 loss, with optional weighting.
`tensorplay.nn.functional.linear_cross_entropy`	Compute cross entropy between `input`, transformed linearly, and target.
`tensorplay.nn.functional.mse_loss`
`tensorplay.nn.functional.margin_ranking_loss`	Compute the margin ranking loss.
`tensorplay.nn.functional.multilabel_margin_loss`	Compute the multilabel margin loss.
`tensorplay.nn.functional.multilabel_soft_margin_loss`	Compute the multilabel soft margin loss.
`tensorplay.nn.functional.multi_margin_loss`	Compute the multi margin loss, with optional weighting.
`tensorplay.nn.functional.nll_loss`	The negative log likelihood loss.
`tensorplay.nn.functional.huber_loss`	Compute the Huber loss, with optional weighting.
`tensorplay.nn.functional.smooth_l1_loss`	Compute the Smooth L1 loss.
`tensorplay.nn.functional.soft_margin_loss`	Compute the soft margin loss.
`tensorplay.nn.functional.triplet_margin_loss`	Compute the triplet loss between given input tensors and a margin greater than 0.
`tensorplay.nn.functional.triplet_margin_with_distance_loss`	Compute the triplet margin loss using a custom distance function.

Vision functions

`tensorplay.nn.functional.pixel_shuffle`	Rearranges elements in a tensor of shape `(, C x r^2, H, W)` to a tensor of shape `(, C, H x r, W x r)`.
`tensorplay.nn.functional.pixel_unshuffle`	Reverses the `pixel_shuffle()` transformation: `(, C, H x r, W x r) -> (, C x r^2, H, W)`.
`tensorplay.nn.functional.pad`	Pads tensor.
`tensorplay.nn.functional.interpolate`	interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None) -> Tensor
`tensorplay.nn.functional.grid_sample`	Compute grid sample.
`tensorplay.nn.functional.affine_grid`	Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices `theta`.

Low-Precision functions

`tensorplay.nn.functional.grouped_mm`
`tensorplay.nn.functional.scaled_mm`
`tensorplay.nn.functional.scaled_grouped_mm`

TensorPlay-specific additions

`adaptive_max_pool1d_with_indices`	Applies a 1D adaptive max pooling over an input signal, returning `(output, indices)`.
`adaptive_max_pool2d_with_indices`	Applies a 2D adaptive max pooling over an input signal composed of several input planes, returning `(output, indices)`.
`adaptive_max_pool3d_with_indices`	Applies a 3D adaptive max pooling over an input signal, returning `(output, indices)`.
`celu_`
`channel_shuffle`	Divide the channels in a tensor into `g` groups and rearrange them as in ShuffleNet: `(, C, H, W) -> (, C, H, W)` with channels interleaved across groups.
`conv_tbc`	Applies a 1D convolution over an input of shape (T, B, C) along the `(kernel_width, in_channels, out_channels)`; the math is a standard cross-channel conv1d after permuting to (B, C, T).
`dropout_`	In-place version of `dropout()`.
`feature_dropout`	Randomly zeroes entire channels (dim 1).
`feature_dropout_`	In-place version of `feature_dropout()`.
`flatten`
`fractional_max_pool2d_with_indices`	Applies 2D fractional max pooling over an input signal composed of several input planes, returning `(output, indices)`.
`fractional_max_pool3d_with_indices`	Applies 3D fractional max pooling over an input signal composed of several input planes, returning `(output, indices)`.
`gru_cell`	One time step of a gated recurrent unit cell.
`lstm_cell`	One time step of a long short-term memory cell.
`max_pool1d_with_indices`	Applies a 1D max pooling over an input signal, returning `(output, indices)`.
`max_pool2d_with_indices`	Applies a 2D max pooling over an input composed of several input planes, returning `(output, indices)`.
`max_pool3d_with_indices`	Applies a 3D max pooling over an input signal, returning `(output, indices)`.
`multi_head_attention_forward`	multi_head_attention_forward(query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias=None, bias_k=None, bias_v=None, add_zero_attn=False, dropout_p=0.0, out_proj_weight=None, out_proj_bias=None, training=True, key_padding_mask=None, need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None, v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=True, is_causal=False) -> (Tensor, Optional[Tensor])
`native_channel_shuffle`
`rnn_relu_cell`	One time step of an Elman RNN cell with ReLU nonlinearity.
`rnn_tanh_cell`	One time step of an Elman RNN cell with tanh nonlinearity.
`selu_`