class_docstr: Random uniform initializer.
  
  Draws samples from a uniform distribution for given parameters.
  
  Examples:
  
  >>> # Standalone usage:
  >>> initializer = RandomUniform(minval=0.0, maxval=1.0)
  >>> values = initializer(shape=(2, 2))
  
  >>> # Usage in a Keras layer:
  >>> initializer = RandomUniform(minval=0.0, maxval=1.0)
  >>> layer = Dense(3, kernel_initializer=initializer)
  
  Args:
      minval: A python scalar or a scalar keras tensor. Lower bound of the
          range of random values to generate (inclusive).
      maxval: A python scalar or a scalar keras tensor. Upper bound of the
          range of random values to generate (exclusive).
      seed: A Python integer or instance of
          `keras.backend.SeedGenerator`.
          Used to make the behavior of the initializer
          deterministic. Note that an initializer seeded with an integer
          or `None` (unseeded) will produce the same random values
          across multiple calls. To get different random values
          across multiple calls, use as seed an instance
          of `keras.backend.SeedGenerator`.
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code: def __call__(self, shape, dtype=None):
      return random.uniform(
          shape=shape,
          minval=self.minval,
          maxval=self.maxval,
          seed=self.seed,
          dtype=dtype,
      )
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.backend import random
  from keras.initializers.initializer import Initializer
  from keras.saving import serialization_lib
label_desc: Use image node_id 2 for calling the RandomUniform obj's underlying member method code with example usage: obj.__call__(shape, dtype) and returns: random