class_docstr: Initialize module parameters with the values drawn from the normal
  distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
  
  Args:
      mean (int | float):the mean of the normal distribution. Defaults to 0.
      std (int | float): the standard deviation of the normal distribution.
          Defaults to 1.
      bias (int | 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 __init__(self, mean=0, std=1, **kwargs):
      super().__init__(**kwargs)
      self.mean = mean
      self.std = std
  
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 1 to create a new NormalInit object from inherited base classes: BaseInit with example: obj = NormalInit(mean, std)