code: def __init__(self, num_bags, num_embeddings_per_bag, num_dims):
      super().__init__()
      self.num_bags = num_bags
      self.embedding_bags: nn.ModuleDict = nn.ModuleDict()
  
      for i in range(num_bags):
          self.embedding_bags[f"embedding_bag_{i}"] = nn.EmbeddingBag(
              num_embeddings_per_bag, num_dims, mode="sum"
          )
  
imports: import sys
  import copy
  import torch
  import torch.nn
  from torch.testing._internal.common_distributed import requires_nccl, skip_if_lt_x_gpu
  from torch.distributed._shard import shard_module
  from torch.distributed._shard.sharding_plan import ShardingPlan
  from torch.distributed._shard.sharder import Sharder
  from torch.distributed._shard.sharding_spec import ChunkShardingSpec
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torch.testing._internal.common_utils import TEST_WITH_DEV_DBG_ASAN, run_tests
  from torch.testing._internal.distributed._shard.sharded_tensor import TEST_GPU_NUM, ShardedTensorTestBase, with_comms
label_desc: Use image node_id 1 to create a new CustomEmbeddingBagCollection object from inherited base classes: nn with example: obj = CustomEmbeddingBagCollection(num_bags, num_embeddings_per_bag, num_dims)