tensorflow_probability/python/edward2/random_variable.py
def value(self):
    """Get tensor that the random variable corresponds to."""
    if self._value is None:
      try:
        self._value = self.distribution.sample(self.sample_shape_tensor())
      except NotImplementedError:
        raise NotImplementedError(
            "sample is not implemented for {0}. You must either pass in the "
            "value argument or implement sample for {0}."
            .format(self.distribution.__class__.__name__))
    return self._value
def value(self):
    """Get tensor that the random variable corresponds to."""
    if self._value is None:
      try:
        self._value = self.distribution.sample(self.sample_shape_tensor())
      except NotImplementedError:
        raise NotImplementedError(
            "sample is not implemented for {0}. You must either pass in the "
            "value argument or implement sample for {0}."
            .format(self.distribution.__class__.__name__))
    return self._value
def value ( self ) : if self . _value is None : try : self . _value = self . distribution . sample ( self . sample_shape_tensor ( ) ) except NotImplementedError : raise NotImplementedError ( "sample is not implemented for {0}. You must either pass in the " "value argument or implement sample for {0}." . format ( self . distribution . __class__ . __name__ ) ) return self . _value
Get tensor that the random variable corresponds to.
Get tensor that the random variable corresponds to .