code: def __call__(
      self,
      average_batch_size: int,
      world_size: int,
      num_float_features: int,
      tables: Union[
          List[EmbeddingTableConfig], List[EmbeddingBagConfig]
      ],
      pooling_avg: int = 10,
      global_constant_batch: bool = False,
  ) -> Tuple["ModelInput", List["ModelInput"]]:
      ...
  
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 1 for calling the VariableBatchModelInputCallable obj's underlying member method code with example usage: obj.__call__(average_batch_size, world_size, num_float_features, tables, pooling_avg, global_constant_batch) without return types