class_docstr: Initializes parameters with random values drawn from a uniform distribution.
class_docstr_tok: ["Initializes","parameters","with","random","values","drawn","from","a","uniform","distribution","."]
docstr: Class constructor.
  
  Args:
      low  (float, optional): Lower bound of range.
      high (float, optional): Upper bound of range.
docstr_tok: ["Class","constructor",".","Args",":","low","(","float",",","optional",")",":","Lower","bound","of","range",".","high","(","float",",","optional",")",":","Upper","bound","of","range","."]
code: def __init__(self, low=0.0, high=1.0, name="uniformInit"):
      """
      Class constructor.
  
      Args:
          low  (float, optional): Lower bound of range.
          high (float, optional): Upper bound of range.
      """
      super(Uniform, self).__init__(name=name)
      self.low, self.high = (low, high)
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 1 to create a new Uniform object from inherited base classes: Initializer with example: obj = Uniform(low, high, name)