class_docstr: Initializer that generates tensors with a uniform distribution.
  
  The generated values follow a uniform distribution in the range
  ``[minval, maxval)``.
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docstr: Constructs a random uniform initializer.
  
  Args:
    minval: A python scalar or a scalar tensor. Lower bound of the range of
      random values to generate. Defaults to ``0``.
    maxval: A python scalar or a scalar tensor. Upper bound of the range of
      random values to generate. Defaults to ``1``.
    seed: The seed used in the generation of random numbers.
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code: def __init__(
      self,
      minval: types.FloatLike = 0,
      maxval: types.FloatLike = 1,
      seed: Optional[int] = None,
  ):
      """Constructs a random uniform initializer.
  
      Args:
        minval: A python scalar or a scalar tensor. Lower bound of the range of
          random values to generate. Defaults to ``0``.
        maxval: A python scalar or a scalar tensor. Upper bound of the range of
          random values to generate. Defaults to ``1``.
        seed: The seed used in the generation of random numbers.
      """
      self.minval = minval
      self.maxval = maxval
      self.seed = seed
  
imports: import abc
  import collections
  from typing import Iterable, Mapping, Optional, Union
  import numpy
  from sonnet.src import types
  import tensorflow
label_desc: Use image node_id 1 to create a new RandomUniform object from inherited base classes: Initializer with example: obj = RandomUniform(minval, maxval, seed)