code: def _generate_dmps_and_batch(
      self,
      sharders: Optional[List[ModuleSharder[nn.Module]]] = None,
      constraints: Optional[
          Dict[str, trec_dist.planner.ParameterConstraints]
      ] = None,
  ) -> Tuple[List[DistributedModelParallel], ModelInput]:
      if constraints is None:
          constraints = {}
      if sharders is None:
          sharders = get_default_sharders()
  
      _, local_batch = ModelInput.generate(
          batch_size=self.batch_size,
          world_size=1,
          num_float_features=self.num_float_features,
          tables=self.tables,
          weighted_tables=self.weighted_tables,
      )
      batch = local_batch[0].to(self.device)
  
      dmps = []
      pg = dist.GroupMember.WORLD
      assert pg is not None, "Process group is not initialized"
      env = ShardingEnv.from_process_group(pg)
  
      planner = EmbeddingShardingPlanner(
          topology=Topology(
              local_world_size=trec_dist.comm.get_local_size(
                  env.world_size
              ),
              world_size=env.world_size,
              compute_device=self.device.type,
          ),
          constraints=constraints,
      )
  
      for _ in range(2):
          # Create two TestSparseNN modules, wrap both in DMP
          m = TestSparseNN(
              tables=self.tables,
              num_float_features=self.num_float_features,
              weighted_tables=self.weighted_tables,
              dense_device=self.device,
              sparse_device=torch.device("meta"),
          )
          if pg is not None:
              plan = planner.collective_plan(m, sharders, pg)
          else:
              plan = planner.plan(m, sharders)
  
          dmp = DistributedModelParallel(
              module=m,
              init_data_parallel=False,
              device=self.device,
              sharders=sharders,
              plan=plan,
          )
  
          with torch.no_grad():
              dmp(batch)
              dmp.init_data_parallel()
          dmps.append(dmp)
      return (dmps, batch)
  
imports: import os
  import unittest
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, List, Optional, OrderedDict, Tuple
  import numpy
  import torch
  import torch.nn
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from hypothesis import given, settings, strategies, Verbosity
  from torch import distributed
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torchrec import distributed
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, EmbeddingTableConfig
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder, ShardedEmbeddingBagCollection
  from torchrec.distributed.fused_embeddingbag import ShardedFusedEmbeddingBagCollection
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.sharding_plan import get_default_sharders
  from torchrec.distributed.test_utils.test_model import _get_default_rtol_and_atol, ModelInput, TestSparseNN, TestSparseNNBase
  from torchrec.distributed.test_utils.test_sharding import copy_state_dict, create_test_sharder, gen_model_and_input, ModelInputCallable, SharderType
  from torchrec.distributed.types import ModuleSharder, ShardingEnv, ShardingPlan, ShardingType
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingBagConfig, PoolingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.fused_embedding_modules import FusedEmbeddingBagCollection
  from torchrec.test_utils import get_free_port, seed_and_log
label_desc: Use image node_id 3 for calling the ModelParallelStateDictBase obj's underlying member method code with example usage: obj._generate_dmps_and_batch(sharders, constraints) and returns: dmps,  batch