code: def test_local_global_discriminator():
      import torch
      from deepchem.models.torch_models.gnn import (
          LocalGlobalDiscriminator,
      )
  
      hidden_dim = 10
      discriminator = LocalGlobalDiscriminator(hidden_dim=hidden_dim)
  
      # Create random local node representations and global graph representations
      batch_size = 6
      x = torch.randn(batch_size, hidden_dim)
      summary = torch.randn(batch_size, hidden_dim)
  
      # Compute similarity scores using the discriminator
      similarity_scores = discriminator(x, summary)
  
      # Check if the output has the correct shape and dtype
      assert similarity_scores.shape == (batch_size,)
      assert similarity_scores.dtype == torch.float32
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 47 for calling a global function with example usage: test_local_global_discriminator() without return types