code: def __init__(self, ec: EmbeddingCollection) -> None:
      super().__init__()
      self.ec = ec
      self.over_arch = torch.nn.Linear(
          ec.embedding_dim(),
          1,
      )
  
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 to create a new TestSequentialModel object from inherited base classes: torch.nn with example: obj = TestSequentialModel(ec)