def sample_n(self, n, seed=None, name='sample_n'):
    'Draws `n` samples from the Gamma distribution(s).\n\n    See the doc for tf.random_gamma for further detail.\n\n    Args:\n      n: Python integer, the number of observations to sample from each\n        distribution.\n      seed: Python integer, the random seed for this operation.\n      name: Optional name for the operation.\n\n    Returns:\n      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`\n          with values of type `self.dtype`.\n    '
    with ops.name_scope(self.name, values=[n, self.alpha, self._beta]):
        return random_ops.random_gamma([n], self.alpha, beta=self._beta, dtype=self.dtype, seed=seed, name=name)
Draws `n` samples from the Gamma distribution(s).

See the doc for tf.random_gamma for further detail.

Args:
  n: Python integer, the number of observations to sample from each
    distribution.
  seed: Python integer, the random seed for this operation.
  name: Optional name for the operation.

Returns:
  samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
      with values of type `self.dtype`.
def sample_n(self, n, seed=None, name='sample_n'):
    'Draws `n` samples from the Gamma distribution(s).\n\n    See the doc for tf.random_gamma for further detail.\n\n    Args:\n      n: Python integer, the number of observations to sample from each\n        distribution.\n      seed: Python integer, the random seed for this operation.\n      name: Optional name for the operation.\n\n    Returns:\n      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`\n          with values of type `self.dtype`.\n    '
    with ops.name_scope(self.name, values=[n, self.alpha, self._beta]):
        return random_ops.random_gamma([n], self.alpha, beta=self._beta, dtype=self.dtype, seed=seed, name=name)