class_docstr: Initialize module parameters with values according to the method
  described in `Understanding the difficulty of training deep feedforward
  neural networks - Glorot, X. & Bengio, Y. (2010).
  <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
  
  Args:
      gain (int | float): an optional scaling factor. 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.
      distribution (str): distribution either be ``'normal'``
          or ``'uniform'``. Defaults to ``'normal'``.
      layer (str | list[str], optional): the layer will be initialized.
          Defaults to None.
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code: def _get_init_info(self):
      info = (
          f"{self.__class__.__name__}: gain={self.gain}, "
          f"distribution={self.distribution}, bias={self.bias}"
      )
      return 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 3 for calling the XavierInit obj's underlying member method code with example usage: obj._get_init_info() and returns: info