code: def uniform(
      shape=None, minval=0, maxval=None, dtype=tf.float32, seed=None
  ):
      """Uniform.
  
      Initialization with random values from a uniform distribution.
  
      The generated values follow a uniform distribution in the range
      `[minval, maxval)`. The lower bound `minval` is included in the range,
      while the upper bound `maxval` is excluded.
  
      For floats, the default range is `[0, 1)`.  For ints, at least `maxval`
      must be specified explicitly.
  
      In the integer case, the random integers are slightly biased unless
      `maxval - minval` is an exact power of two.  The bias is small for values of
      `maxval - minval` significantly smaller than the range of the output (either
      `2**32` or `2**64`).
  
      Arguments:
          shape: List of `int`. A shape to initialize a Tensor (optional).
          dtype: The tensor data type. Only float are supported.
          seed: `int`. Used to create a random seed for the distribution.
  
      Returns:
          The Initializer, or an initialized `Tensor` if shape is specified.
  
      """
      if shape:
          return tf.random_uniform(
              shape,
              minval=minval,
              maxval=maxval,
              seed=seed,
              dtype=dtype,
          )
      else:
          return tf.random_uniform_initializer(
              minval=minval, maxval=maxval, seed=seed, dtype=dtype
          )
  
file_path: tflearn/tflearn/initializations.py
imports: from __future__ import division, print_function, absolute_import
  import math
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 3 for calling a global function with example usage: uniform(shape, minval, maxval, dtype, seed) and returns: tf, tf