code: def verify_net_in_out(
      net_id: str | None = None,
      meta_file: str | Sequence[str] | None = None,
      config_file: str | Sequence[str] | None = None,
      device: str | None = None,
      p: int | None = None,
      n: int | None = None,
      any: int | None = None,
      extra_forward_args: dict | None = None,
      args_file: str | None = None,
      **override: Any,
  ) -> None:
      """
      Verify the input and output data shape and data type of network defined in the metadata.
      Will test with fake Tensor data according to the required data shape in `metadata`.
  
      Typical usage examples:
  
      .. code-block:: bash
  
          python -m monai.bundle verify_net_in_out network --meta_file <meta path> --config_file <config path>
  
      Args:
          net_id: ID name of the network component to verify, it must be `torch.nn.Module`.
          meta_file: filepath of the metadata file to get network args, if `None`, must be provided in `args_file`.
              if it is a list of file paths, the content of them will be merged.
          config_file: filepath of the config file to get network definition, if `None`, must be provided in `args_file`.
              if it is a list of file paths, the content of them will be merged.
          device: target device to run the network forward computation, if None, prefer to "cuda" if existing.
          p: power factor to generate fake data shape if dim of expected shape is "x**p", default to 1.
          n: multiply factor to generate fake data shape if dim of expected shape is "x*n", default to 1.
          any: specified size to generate fake data shape if dim of expected shape is "*", default to 1.
          extra_forward_args: a dictionary that contains other args for the forward function of the network.
              Default to an empty dictionary.
          args_file: a JSON or YAML file to provide default values for `net_id`, `meta_file`, `config_file`,
              `device`, `p`, `n`, `any`, and override pairs. so that the command line inputs can be simplified.
          override: id-value pairs to override or add the corresponding config content.
              e.g. ``--_meta#network_data_format#inputs#image#num_channels 3``.
  
      """
  
      _args = update_kwargs(
          args=args_file,
          net_id=net_id,
          meta_file=meta_file,
          config_file=config_file,
          device=device,
          p=p,
          n=n,
          any=any,
          extra_forward_args=extra_forward_args,
          **override,
      )
      _log_input_summary(tag="verify_net_in_out", args=_args)
      (
          config_file_,
          meta_file_,
          net_id_,
          device_,
          p_,
          n_,
          any_,
          extra_forward_args_,
      ) = _pop_args(
          _args,
          "config_file",
          "meta_file",
          net_id="",
          device="cuda:0" if is_available() else "cpu",
          p=1,
          n=1,
          any=1,
          extra_forward_args={},
      )
  
      parser = ConfigParser()
      parser.read_config(f=config_file_)
      parser.read_meta(f=meta_file_)
  
      # the rest key-values in the _args are to override config content
      for k, v in _args.items():
          parser[k] = v
  
      (
          input_channels,
          input_spatial_shape,
          input_dtype,
          output_channels,
          output_dtype,
      ) = _get_net_io_info(parser=parser)
      try:
          key: str = net_id_  # mark the full id when KeyError
          net = parser.get_parsed_content(key).to(device_)
      except KeyError as e:
          raise KeyError(
              f"Failed to verify due to missing expected key in the config: {key}."
          ) from e
  
      net.eval()
      with torch.no_grad():
          spatial_shape = _get_fake_spatial_shape(
              input_spatial_shape, p=p_, n=n_, any=any_
          )
          test_data = torch.rand(
              *(1, input_channels, *spatial_shape),
              dtype=input_dtype,
              device=device_,
          )
          if input_dtype == torch.float16:
              # fp16 can only be executed in gpu mode
              net.to("cuda")
              from torch.cuda.amp import autocast
  
              with autocast():
                  output = net(test_data.cuda(), **extra_forward_args_)
              net.to(device_)
          else:
              output = net(test_data, **extra_forward_args_)
          if output.shape[1] != output_channels:
              raise ValueError(
                  f"output channel number `{output.shape[1]}` doesn't match: `{output_channels}`."
              )
          if output.dtype != output_dtype:
              raise ValueError(
                  f"dtype of output data `{output.dtype}` doesn't match: {output_dtype}."
              )
      logger.info("data shape of network is verified with no error.")
  
imports: from __future__ import annotations
  import ast
  import json
  import os
  import re
  import warnings
  from collections.abc import Mapping, Sequence
  from pathlib import Path
  from pydoc import locate
  from shutil import copyfile
  from textwrap import dedent
  from typing import Any, Callable
  import torch
  from torch.cuda import is_available
  from monai.apps.mmars.mmars import _get_all_ngc_models
  from monai.apps.utils import _basename, download_url, extractall, get_logger
  from monai.bundle.config_item import ConfigComponent
  from monai.bundle.config_parser import ConfigParser
  from monai.bundle.utils import DEFAULT_INFERENCE, DEFAULT_METADATA
  from monai.bundle.workflows import BundleWorkflow, ConfigWorkflow
  from monai.config import IgniteInfo, PathLike
  from monai.data import load_net_with_metadata, save_net_with_metadata
  from monai.networks import convert_to_onnx, convert_to_torchscript, convert_to_trt, copy_model_state, get_state_dict, save_state
  from monai.utils import check_parent_dir, deprecated_arg, ensure_tuple, get_equivalent_dtype, min_version, optional_import, pprint_edges
label_desc: Use image node_id 28 for calling a global function with example usage: verify_net_in_out(net_id, meta_file, config_file, device, p, n, any, extra_forward_args, args_file) without return types