class_docstr: Initializer that generates tensors with a uniform distribution.
  It draws samples from a uniform distribution within [lower, upper]
  If lower and upper is not specified, it draws samples form a
  uniform distribution within [-limit, limit] where "limit" is "1/sqrt(fan_in)"
class_docstr_tok: ["Initializer","that","generates","tensors","with","a","uniform","distribution",".","It","draws","samples","from","a","uniform","distribution","within","[","lower",",","upper","]","If","lower","and","upper","is","not","specified",",","it","draws","samples","form","a","uniform","distribution","within","[","-limit",",","limit","]","where","``","limit","''","is","``","1\/sqrt","(","fan_in",")","''"]
code: def __init__(self, upper=None, lower=None, bigdl_type="float"):
      if upper is not None and lower is not None:
          upper = upper + 0.0
          lower = lower + 0.0
          JavaValue.__init__(self, None, bigdl_type, upper, lower)
      else:
          JavaValue.__init__(self, None, bigdl_type)
  
imports: import sys
  from bigdl.dllib.utils.common import JavaValue
label_desc: Use image node_id 1 to create a new RandomUniform object from inherited base classes: InitializationMethod with example: obj = RandomUniform(upper, lower, bigdl_type)