code: def __init__(self, num_embeddings, init_max, init_min, batch_size):
      super().__init__()
      self.embedding_dim = 16
      self.batch_size = batch_size
      self.config = EmbeddingConfig(
          name="id",
          embedding_dim=self.embedding_dim,
          num_embeddings=num_embeddings,
          weight_init_max=init_max,
          weight_init_min=init_min,
      )
      self.emb = EmbeddingCollection(
          tables=[self.config], device=torch.device("meta")
      )
      self.dense = nn.Linear(16, 1)
  
imports: import unittest
  import torch
  import torch.distributed
  import torch.nn
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torchrec import EmbeddingCollection, EmbeddingConfig, KeyedJaggedTensor
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.fused_embeddingbag import FusedEmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from torchrec_dynamic_embedding.id_transformer_group import IDTransformerGroup
  from utils import init_dist, register_memory_io
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(num_embeddings, init_max, init_min, batch_size)