code: def _convert_module(
      module: nn.Module,
      input_tensor_size_lut: Dict,
      base_name: str = "",
      convert_for_quantize: bool = False,
      native_conv3d_op_qnnpack: bool = False,
  ) -> None:
      """
      This helper function recursively goes through sub-modules in a network. If current
      module is a efficient block (instance of EfficientBlockBase) with convert() method,
      its convert() method will be called, and the input tensor size (needed by efficient
      blocks for mobile cpu) will be provided by matching module name in
      input_tensor_size_lut.
      Otherwise if the input module is a non efficient block, this function will try to go
      through child modules of input module to look for any efficient block in lower
      hierarchy.
      Args:
          module (nn.Module): input module for convert.
          input_tensor_size_lut (dict): input tensor size look-up table.
          base_name (str): module name for input module.
          convert_for_quantize (bool): whether this module is intended to be quantized.
          native_conv3d_op_qnnpack (bool): whether the QNNPACK version has native int8
              Conv3d.
      """
      if isinstance(module, EfficientBlockBase):
          module.convert(
              input_tensor_size_lut[base_name],
              convert_for_quantize=convert_for_quantize,
              native_conv3d_op_qnnpack=native_conv3d_op_qnnpack,
          )
      else:
          for name, child in module.named_children():
              _convert_module(
                  child,
                  input_tensor_size_lut,
                  base_name=f"{base_name}.{name}",
                  convert_for_quantize=convert_for_quantize,
                  native_conv3d_op_qnnpack=native_conv3d_op_qnnpack,
              )
  
imports: from copy import deepcopy
  from typing import Dict, List
  import torch
  import torch.nn
  from pytorchvideo.accelerator.efficient_blocks.efficient_block_base import EfficientBlockBase
label_desc: Use image node_id 2 for calling a global function with example usage: _convert_module(module, input_tensor_size_lut, base_name, convert_for_quantize, native_conv3d_op_qnnpack) without return types