def __init__(self, rate, validate_args=False, allow_nan_stats=True, name='Exponential'):
    'Construct Exponential distribution with parameter `rate`.\n\n    Args:\n      rate: Floating point tensor, equivalent to `1 / mean`. Must contain only\n        positive values.\n      validate_args: Python `bool`, default `False`. When `True` distribution\n        parameters are checked for validity despite possibly degrading runtime\n        performance. When `False` invalid inputs may silently render incorrect\n        outputs.\n      allow_nan_stats: Python `bool`, default `True`. When `True`, statistics\n        (e.g., mean, mode, variance) use the value "`NaN`" to indicate the\n        result is undefined. When `False`, an exception is raised if one or\n        more of the statistic\'s batch members are undefined.\n      name: Python `str` name prefixed to Ops created by this class.\n    '
    parameters = locals()
    with ops.name_scope(name, values=[rate]):
        self._rate = ops.convert_to_tensor(rate, name='rate')
    super(Exponential, self).__init__(concentration=array_ops.ones([], dtype=self._rate.dtype), rate=self._rate, allow_nan_stats=allow_nan_stats, validate_args=validate_args, name=name)
    self._reparameterization_type = True
    self._parameters = parameters
    self._graph_parents += [self._rate]
Construct Exponential distribution with parameter `rate`.

Args:
  rate: Floating point tensor, equivalent to `1 / mean`. Must contain only
    positive values.
  validate_args: Python `bool`, default `False`. When `True` distribution
    parameters are checked for validity despite possibly degrading runtime
    performance. When `False` invalid inputs may silently render incorrect
    outputs.
  allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
    (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
    result is undefined. When `False`, an exception is raised if one or
    more of the statistic's batch members are undefined.
  name: Python `str` name prefixed to Ops created by this class.
def __init__(self, rate, validate_args=False, allow_nan_stats=True, name='Exponential'):
    'Construct Exponential distribution with parameter `rate`.\n\n    Args:\n      rate: Floating point tensor, equivalent to `1 / mean`. Must contain only\n        positive values.\n      validate_args: Python `bool`, default `False`. When `True` distribution\n        parameters are checked for validity despite possibly degrading runtime\n        performance. When `False` invalid inputs may silently render incorrect\n        outputs.\n      allow_nan_stats: Python `bool`, default `True`. When `True`, statistics\n        (e.g., mean, mode, variance) use the value "`NaN`" to indicate the\n        result is undefined. When `False`, an exception is raised if one or\n        more of the statistic\'s batch members are undefined.\n      name: Python `str` name prefixed to Ops created by this class.\n    '
    parameters = locals()
    with ops.name_scope(name, values=[rate]):
        self._rate = ops.convert_to_tensor(rate, name='rate')
    super(Exponential, self).__init__(concentration=array_ops.ones([], dtype=self._rate.dtype), rate=self._rate, allow_nan_stats=allow_nan_stats, validate_args=validate_args, name=name)
    self._reparameterization_type = True
    self._parameters = parameters
    self._graph_parents += [self._rate]