class_docstr: Sample initial weights from the uniform distribution.
  
  Parameters are sampled from U(a, b).
  
  Parameters
  ----------
  range : float or tuple
      When std is None then range determines a, b. If range is a float the
      weights are sampled from U(-range, range). If range is a tuple the
      weights are sampled from U(range[0], range[1]).
  std : float or None
      If std is a float then the weights are sampled from
      U(mean - np.sqrt(3) * std, mean + np.sqrt(3) * std).
  mean : float
      see std for description.
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code: def sample(self, shape):
      return floatX(
          get_rng().uniform(
              low=self.range[0], high=self.range[1], size=shape
          )
      )
  
imports: import numpy
  from .utils import floatX
  from .random import get_rng
label_desc: Use image node_id 2 for calling the Uniform obj's underlying member method code with example usage: obj.sample(shape) and returns: floatX