code: def _get_module_size(module: nn.Module, multiplier: float) -> int:
      parameters_size = sum(
          [
              multiplier * get_tensor_size_bytes(parameter)
              for parameter in module.parameters()
          ]
      )
  
      buffers_size = sum(
          [get_tensor_size_bytes(buffer) for buffer in module.buffers()]
      )
  
      return round(parameters_size + buffers_size)
  
imports: import copy
  import logging
  import math
  from typing import Dict, List, Optional, Set, Tuple
  from torch import nn
  from torchrec.distributed.planner.constants import BIGINT_DTYPE, POOLING_FACTOR
  from torchrec.distributed.planner.types import ParameterConstraints, PlannerError, PlannerErrorType, Storage, StorageReservation, Topology
  from torchrec.distributed.planner.utils import sharder_name, storage_repr_in_gb
  from torchrec.distributed.types import get_tensor_size_bytes, ModuleSharder
label_desc: Use image node_id 1 for calling a global function with example usage: _get_module_size(module, multiplier) and returns: round