The graph convolutional operator with initial residual connections and identity mapping (GCNII) from the "Simple and Deep Graph Convolutional Networks" paper. Convert the output of Captum attribution methods which is a tuple of attributions to two dictionaries with node and edge attribution tensors.

The (translation-invariant) feature-steered convolutional operator from the "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis" paper.

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.

BatchNorm1d module with lazy initialization of the num_features argument of the BatchNorm1d that is inferred from the input. Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension.The self-attention pooling operator from the "Self-Attention Graph Pooling" and "Understanding Attention and Generalization in Graph Neural Networks" papers. The equilibrium aggregation layer from the "Equilibrium Aggregation: Encoding Sets via Optimization" 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. Applies the Exponential Linear Unit (ELU) function, element-wise, as described in the paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).