class_docstr: Initialize module parameters with constant values.
  
  Args:
      val (int | float): the value to fill the weights in the module with
      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.
class_docstr_tok: ["Initialize","module","parameters","with","constant","values",".","Args",":","val","(","int","|","float",")",":","the","value","to","fill","the","weights","in","the","module","with","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","."]
code: def _get_init_info(self):
      info = (
          f"{self.__class__.__name__}: val={self.val}, 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 ConstantInit obj's underlying member method code with example usage: obj._get_init_info() and returns: info