class_docstr: Initialize module parameters with the values drawn from the normal
  distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values
  outside :math:`[a, b]`.
  
  Args:
      mean (float): the mean of the normal distribution. Defaults to 0.
      std (float):  the standard deviation of the normal distribution.
          Defaults to 1.
      a (float): The minimum cutoff value.
      b ( float): The maximum cutoff value.
      bias (float): the value to fill the bias. Defaults to 0.
      bias_prob (float, optional): the probability for bias initialization.
          Defaults to None.
      layer (str | list[str], optional): the layer will be initialized.
          Defaults to None.
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code: def __call__(self, module: nn.Module) -> None:
      def init(m):
          if self.wholemodule:
              trunc_normal_init(
                  m, self.mean, self.std, self.a, self.b, self.bias
              )
          else:
              layername = m.__class__.__name__
              basesname = _get_bases_name(m)
              if len(set(self.layer) & set([layername] + basesname)):
                  trunc_normal_init(
                      m, self.mean, self.std, self.a, self.b, self.bias
                  )
  
      module.apply(init)
      if hasattr(module, "_params_init_info"):
          update_init_info(module, init_info=self._get_init_info())
  
imports: import copy
  import math
  import warnings
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
label_desc: Use image node_id 2 for calling the TruncNormalInit obj's underlying member method code with example usage: obj.__call__(module) without return types