code: def convert_to_deployable_form(
      model: nn.Module,
      input_tensor: torch.Tensor,
      convert_for_quantize: bool = False,
      native_conv3d_op_qnnpack: bool = False,
  ) -> nn.Module:
      """
      This function takes an input model, and returns a deployable model copy.
      Args:
          model (nn.Module): input model for conversion. The model can include a mix of
              efficient blocks (instances of EfficientBlockBase) and non efficient blocks.
              The efficient blocks will be converted by calling its convert() method, while
              other blocks will stay unchanged.
          input_tensor (torch.Tensor): input tensor for model. Note current conversion for
              deployable form in mobile cpu only works for single input tensor size (i.e.,
              the future input tensor to converted model should have the same size as
              input_tensor specified here).
          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.
      """
      input_tensor_size_lut = {}
      hook_handle_list = []
      _add_input_tensor_size_lut_hook(
          model, input_tensor_size_lut, hook_handle_list
      )
      # Run forward to fill in input tensor lut.
      model.eval()
      model(input_tensor)
      # Remove forward hooks.
      for handle in hook_handle_list:
          handle.remove()
      model_converted = deepcopy(model)
      model_converted.eval()
      _convert_module(
          model_converted,
          input_tensor_size_lut,
          convert_for_quantize=convert_for_quantize,
          native_conv3d_op_qnnpack=native_conv3d_op_qnnpack,
      )
      return model_converted
  
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 3 for calling a global function with example usage: convert_to_deployable_form(model, input_tensor, convert_for_quantize, native_conv3d_op_qnnpack) and returns: model_converted