The heterogeneous edge-enhanced graph attentional operator from the "Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction" paper.

Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x x x and target y y y of size ( N , C ) (N, C) ( N , C ). Performs GRU aggregation in which the elements to aggregate are interpreted as a sequence, as described in the "Graph Neural Networks with Adaptive Readouts" paper. The Weisfeiler Lehman (WL) operator from the "A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction" paper.Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . The local extremum graph neural network operator from the "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations" paper. The PointNet set layer from the "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" and "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" papers.

Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input. Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1. A PyTorch module that implements the equivariant vector-scalar interactive graph neural network (ViSNet) from the "Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive Message Passing" paper. The anti-symmetric graph convolutional operator from the "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" paper.Performs aggregations with one or more aggregators and combines aggregated results, as described in the "Principal Neighbourhood Aggregation for Graph Nets" and "Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions" papers. The edge pooling operator from the "Towards Graph Pooling by Edge Contraction" and "Edge Contraction Pooling for Graph Neural Networks" papers. The continuous kernel-based convolutional operator from the "Neural Message Passing for Quantum Chemistry" paper.

Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.

Applies layer normalization by subtracting the mean from the inputs as described in the "Revisiting 'Over-smoothing' in Deep GCNs" paper. Allows the model to jointly attend to information from different representation subspaces as described in the paper: Attention Is All You Need. The MetaPath2Vec model from the "metapath2vec: Scalable Representation Learning for Heterogeneous Networks" paper where random walks based on a given metapath are sampled in a heterogeneous graph, and node embeddings are learned via negative sampling optimization.

The self-attention pooling operator from the "Self-Attention Graph Pooling" and "Understanding Attention and Generalization in Graph Neural Networks" papers.The relational graph convolutional operator from the "Modeling Relational Data with Graph Convolutional Networks" paper.