code: def _assert_dense_list_equality(
      self,
      dense_list_1: List[torch.Tensor],
      dense_list_2: List[torch.Tensor],
  ) -> None:
      self.assertEqual(len(dense_list_1), len(dense_list_2))
      for tensor_1, tensor_2 in zip(dense_list_1, dense_list_2):
          torch.testing.assert_close(tensor_1, tensor_2, rtol=0, atol=0)
  
imports: import unittest
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple, Type
  import hypothesis.strategies
  import torch
  import torch.fx
  import torchrec
  from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
  from hypothesis import given, settings
  from torchrec.modules.embedding_configs import EmbeddingBagConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.modules.fused_embedding_modules import fuse_embedding_optimizer, FusedEmbeddingBagCollection, FusedEmbeddingCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 for calling the FusedEmbeddingCollectionTest obj's underlying member method code with example usage: obj._assert_dense_list_equality(dense_list_1, dense_list_2) without return types