code: def normal_init(
      m,
      std: float = 0.02,
      normal_func: Callable[
          [torch.Tensor, float, float], Any
      ] = torch.nn.init.normal_,
  ) -> None:
      """
      Initialize the weight and bias tensors of `m' and its submodules to values from a normal distribution with a
      stddev of `std'. Weight tensors of convolution and linear modules are initialized with a mean of 0, batch
      norm modules with a mean of 1. The callable `normal_func', used to assign values, should have the same arguments
      as its default normal_(). This can be used with `nn.Module.apply` to visit submodules of a network.
      """
      cname = m.__class__.__name__
  
      if getattr(m, "weight", None) is not None and (
          cname.find("Conv") != -1 or cname.find("Linear") != -1
      ):
          normal_func(m.weight.data, 0.0, std)
          if getattr(m, "bias", None) is not None:
              nn.init.constant_(m.bias.data, 0.0)
  
      elif cname.find("BatchNorm") != -1:
          normal_func(m.weight.data, 1.0, std)
          nn.init.constant_(m.bias.data, 0)
  
imports: from __future__ import annotations
  import io
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Mapping, Sequence
  from contextlib import contextmanager
  from copy import deepcopy
  from typing import Any
  import numpy
  import torch
  import torch.nn
  from monai.apps.utils import get_logger
  from monai.config import PathLike
  from monai.utils.misc import ensure_tuple, save_obj, set_determinism
  from monai.utils.module import look_up_option, optional_import, pytorch_after
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 8 for calling a global function with example usage: normal_init(m, std, normal_func) without return types