code: def gen_model_and_input(
      model_class: TestSparseNNBase,
      tables: List[EmbeddingTableConfig],
      embedding_groups: Dict[str, List[str]],
      world_size: int,
      generate: Union[
          ModelInputCallable, VariableBatchModelInputCallable
      ] = ModelInput.generate,
      weighted_tables: Optional[List[EmbeddingTableConfig]] = None,
      num_float_features: int = 16,
      dense_device: Optional[torch.device] = None,
      sparse_device: Optional[torch.device] = None,
      dedup_feature_names: Optional[List[str]] = None,
      dedup_tables: Optional[List[EmbeddingTableConfig]] = None,
      variable_batch_size: bool = False,
      batch_size: int = 4,
      feature_processor_modules: Optional[
          Dict[str, torch.nn.Module]
      ] = None,
      long_indices: bool = True,
      global_constant_batch: bool = False,
  ) -> Tuple[nn.Module, List[Tuple[ModelInput, List[ModelInput]]]]:
      torch.manual_seed(0)
      if dedup_feature_names:
          model = model_class(
              tables=cast(
                  List[BaseEmbeddingConfig],
                  tables + dedup_tables if dedup_tables else tables,
              ),
              num_float_features=num_float_features,
              dedup_feature_names=dedup_feature_names,
              weighted_tables=cast(
                  List[BaseEmbeddingConfig], weighted_tables
              ),
              embedding_groups=embedding_groups,
              dense_device=dense_device,
              sparse_device=sparse_device,
              feature_processor_modules=feature_processor_modules,
          )
      else:
          model = model_class(
              tables=cast(
                  List[BaseEmbeddingConfig],
                  tables,
              ),
              num_float_features=num_float_features,
              weighted_tables=cast(
                  List[BaseEmbeddingConfig], weighted_tables
              ),
              embedding_groups=embedding_groups,
              dense_device=dense_device,
              sparse_device=sparse_device,
              feature_processor_modules=feature_processor_modules,
          )
      inputs = [
          cast(VariableBatchModelInputCallable, generate)(
              average_batch_size=batch_size,
              world_size=world_size,
              num_float_features=num_float_features,
              tables=tables,
              global_constant_batch=global_constant_batch,
          )
          if generate == ModelInput.generate_variable_batch_input
          else cast(ModelInputCallable, generate)(
              world_size=world_size,
              tables=tables,
              dedup_tables=dedup_tables,
              weighted_tables=weighted_tables or [],
              num_float_features=num_float_features,
              variable_batch_size=variable_batch_size,
              batch_size=batch_size,
              long_indices=long_indices,
          )
      ]
      return (model, inputs)
  
imports: from enum import Enum
  from typing import Any, cast, Dict, List, Optional, Protocol, Tuple, Type, Union
  import torch
  import torch.distributed
  import torch.nn
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torch.distributed.optim import _apply_optimizer_in_backward
  from torchrec.distributed.embedding_types import EmbeddingTableConfig
  from torchrec.distributed.fbgemm_qcomm_codec import CommType, get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.test_utils.multi_process import MultiProcessContext
  from torchrec.distributed.test_utils.test_model import ModelInput, TestEBCSharder, TestEBSharder, TestETCSharder, TestETSharder, TestSparseNNBase
  from torchrec.distributed.types import EmbeddingModuleShardingPlan, ModuleSharder, ShardedTensor, ShardingEnv, ShardingPlan, ShardingType
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingBagConfig
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
label_desc: Use image node_id 2 for calling a global function with example usage: gen_model_and_input(model_class, tables, embedding_groups, world_size, generate, weighted_tables, num_float_features, dense_device, sparse_device, dedup_feature_names, dedup_tables, variable_batch_size, batch_size, feature_processor_modules, long_indices, global_constant_batch) and returns: model,  inputs