code: def test_dtnn_embedding():
      """Test invoking the Torch Equivalent of DTNNEmbedding."""
      # Embeddings and results from Tensorflow implementation
      embeddings_tf = [
          [0.51979446, -0.43430394, -0.73670053, -0.443037, 0.6706989],
          [
              0.21077824,
              -0.62696636,
              0.66158307,
              -0.25795913,
              0.31941652,
          ],
          [-0.26653743, 0.15180665, 0.21961051, -0.7263894, -0.4521287],
          [0.64324486, -0.66274744, 0.2814387, 0.5478991, -0.32046735],
          [0.1925143, -0.5505201, -0.35381562, -0.7409675, 0.6427947],
      ]
      results_tf = [
          [0.64324486, -0.66274744, 0.2814387, 0.5478991, -0.32046735],
          [-0.26653743, 0.15180665, 0.21961051, -0.7263894, -0.4521287],
          [0.1925143, -0.5505201, -0.35381562, -0.7409675, 0.6427947],
      ]
      embedding_layer_torch = torch_layers.DTNNEmbedding(
          5, 5, "xavier_uniform_"
      )
      embedding_layer_torch.embedding_list = torch.nn.Parameter(
          torch.tensor(embeddings_tf)
      )
      result_torch = embedding_layer_torch(torch.tensor([3, 2, 4]))
      assert torch.allclose(torch.tensor(results_tf), result_torch)
      assert result_torch.shape == (3, 5)
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 49 for calling a global function with example usage: test_dtnn_embedding() without return types