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Applies the log ⁡ ( Softmax ( x ) ) \log(\text{Softmax}(x)) lo g ( Softmax ( x )) function to an n-dimensional input Tensor. Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

The directional message passing neural network (DimeNet) from the "Directional Message Passing for Molecular Graphs" paper.g., the j j j-th channel of the i i i-th sample in the batched input is a 1D tensor input [ i , j ] \text{input}[i, j] input [ i , j ]). num_features , 64 ), 'x, edge_index -> x1' ), ReLU ( inplace = True ), ( GCNConv ( 64 , 64 ), 'x1, edge_index -> x2' ), ReLU ( inplace = True ), ( lambda x1 , x2 : [ x1 , x2 ], 'x1, x2 -> xs' ), ( JumpingKnowledge ( "cat" , 64 , num_layers = 2 ), 'xs -> x' ), ( global_mean_pool , 'x, batch -> x' ), Linear ( 2 * 64 , dataset .

Applies the Softplus function Softplus ( x ) = 1 β ∗ log ⁡ ( 1 + exp ⁡ ( β ∗ x ) ) \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) Softplus ( x ) = β 1 ​ ∗ lo g ( 1 + exp ( β ∗ x )) element-wise.

The graph attentional propagation layer from the "Attention-based Graph Neural Network for Semi-Supervised Learning" paper. The Heterogeneous Graph Transformer (HGT) operator from the "Heterogeneous Graph Transformer" paper.

The path integral based pooling operator from the "Path Integral Based Convolution and Pooling for Graph Neural Networks" paper. Applies batch normalization over a batch of heterogeneous features as described in the "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper. The (translation-invariant) feature-steered convolutional operator from the "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis" paper. The Frequency Adaptive Graph Convolution operator from the "Beyond Low-Frequency Information in Graph Convolutional Networks" paper. InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input.

Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input x x x (a 2D mini-batch Tensor) and output y y y (which is a 1D tensor of target class indices, 0 ≤ y ≤ x. The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" paper.

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