code: def forward(self, x):
      embeddings = (
          self.emb(x)["id"]
          .values()
          .reshape((self.batch_size, -1, self.embedding_dim))
      )
      fused = embeddings.sum(dim=1)
      output = self.dense(fused)
      pred = torch.sigmoid(output)
      return pred
  
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 2 for calling the Model obj's underlying member method code with example usage: obj.forward(x) and returns: pred