class_docstr: Initialize parameter sampling from the uniform
  distribution.
  
  Parameters
  ----------
  minval : int, float
      Minimum possible value.
  
  maxval : int, float
      Maximum possible value.
  
  seed : None or int
      Random seed. Integer value will make results reproducible.
      Defaults to ``None``.
  
  Methods
  -------
  {Initializer.Methods}
class_docstr_tok: ["Initialize","parameter","sampling","from","the","uniform","distribution",".","Parameters","--","--","--","--","--","minval",":","int",",","float","Minimum","possible","value",".","maxval",":","int",",","float","Maximum","possible","value",".","seed",":","None","or","int","Random","seed",".","Integer","value","will","make","results","reproducible",".","Defaults","to","``","None","``",".","Methods","--","--","--","-","{","Initializer.Methods","}"]
code: def __init__(self, minval=0, maxval=1, seed=None):
      self.minval = minval
      self.maxval = maxval
      self.seed = seed
  
imports: import abc
  import six
  import numpy
  import tensorflow
  from neupy.core.docs import SharedDocsABCMeta
label_desc: Use image node_id 1 to create a new Uniform object from inherited base classes: Initializer with example: obj = Uniform(minval, maxval, seed)