class_docstr: This discriminator module is a linear layer without bias, used to measure the similarity between local node representations (`x`) and global graph representations (`summary`).
  
  The goal of the discriminator is to distinguish between positive and negative pairs of local and global representations.
  
  Examples
  --------
  >>> import torch
  >>> from deepchem.models.torch_models.gnn import LocalGlobalDiscriminator
  >>> discriminator = LocalGlobalDiscriminator(hidden_dim=64)
  >>> x = torch.randn(32, 64)  # Local node representations
  >>> summary = torch.randn(32, 64)  # Global graph representations
  >>> similarity_scores = discriminator(x, summary)
  >>> print(similarity_scores.shape)
  torch.Size([32])
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code: def reset_parameters(self):
      size = self.weight.size(0)
      uniform(size, self.weight)
  
imports: import random
  import copy
  import torch
  import numpy
  from torch_geometric.nn import GINEConv, GCNConv, GATConv, SAGEConv, global_add_pool, global_mean_pool, global_max_pool
  from torch_geometric.nn.aggr import AttentionalAggregation, Set2Set
  from torch_geometric.nn.inits import uniform
  import torch.nn
  from torch.functional import F
  from deepchem.data import Dataset
  from deepchem.models.losses import SoftmaxCrossEntropy, EdgePredictionLoss, GraphNodeMaskingLoss, GraphEdgeMaskingLoss, DeepGraphInfomaxLoss, GraphContextPredLoss
  from deepchem.models.torch_models import ModularTorchModel
  from deepchem.feat.graph_data import BatchGraphData, GraphData, shortest_path_length
  from typing import Iterable, List, Tuple
  from deepchem.metrics import to_one_hot
label_desc: Use image node_id 2 for calling the LocalGlobalDiscriminator obj's underlying member method code with example usage: obj.reset_parameters() without return types