class_docstr: Initialize module parameters with values drawn from the uniform
  distribution :math:`\mathcal{U}(a, b)`.
  
  Args:
      a (int | float): the lower bound of the uniform distribution.
          Defaults to 0.
      b (int | float): the upper bound of the uniform 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, a=0, b=1, **kwargs):
      super().__init__(**kwargs)
      self.a = a
      self.b = b
  
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 UniformInit object from inherited base classes: BaseInit with example: obj = UniformInit(a, b)