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Applies the Softmin 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. Creates a criterion that measures the triplet loss given input tensors a a a, p p p, and n n n (representing anchor, positive, and negative examples, respectively), and a nonnegative, real-valued function ("distance function") used to compute the relationship between the anchor and positive example ("positive distance") and the anchor and negative example ("negative distance"). The Adaptive Structure Aware Pooling operator from the "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations" paper. The label embedding and masking layer from the "Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification" paper.

A greedy clustering algorithm from the "Weighted Graph Cuts without Eigenvectors: A Multilevel Approach" paper of picking an unmarked vertex and matching it with one of its unmarked neighbors (that maximizes its edge weight). Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x x x (a 2D mini-batch Tensor) and output y y y (which is a 2D Tensor of target class indices). Applies graph normalization over individual graphs as described in the "GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training" paper. ConvTranspose3d module with lazy initialization of the in_channels argument of the ConvTranspose3d that is inferred from the input. The heterogeneous edge-enhanced graph attentional operator from the "Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction" paper.ConvTranspose2d module with lazy initialization of the in_channels argument of the ConvTranspose2d that is inferred from the input. 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 ).

Applies pair normalization over node features as described in the "PairNorm: Tackling Oversmoothing in GNNs" paper. The Attentive FP model for molecular representation learning from the "Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism" paper, based on graph attention mechanisms. The graph convolutional operator from the "Semi-supervised Classification with Graph Convolutional Networks" paper. Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . The powermean aggregation operator based on a power term, as described in the "DeeperGCN: All You Need to Train Deeper GCNs" paper.The RotatE model from the "RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space" paper. The spline-based convolutional operator from the "SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels" paper. The graph transformer operator from the "Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification" paper.

Applies Graph Size Normalization over each individual graph in a batch of node features as described in the "Benchmarking Graph Neural Networks" paper. The Principal Neighbourhood Aggregation graph convolution operator from the "Principal Neighbourhood Aggregation for Graph Nets" paper. 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. Creates a criterion that optimizes a two-class classification logistic loss between input tensor x x x and target tensor y y y (containing 1 or -1).