parameters: (shape,
                   alpha,
                   beta=None,
                   dtype=dtypes.float32,
                   seed=None,
                   name=None)
docstring: Draws `shape` samples from each of the given Gamma distribution(s).
  
    `alpha` is the shape parameter describing the distribution(s), and `beta` is
    the inverse scale parameter(s).
  
    Example:
  
      samples = tf.random_gamma([10], [0.5, 1.5])
      # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
      # the samples drawn from each distribution
  
      samples = tf.random_gamma([7, 5], [0.5, 1.5])
      # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
      # represents the 7x5 samples drawn from each of the two distributions
  
      samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]])
      # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.
  
    Args:
      shape: A 1-D integer Tensor or Python array. The shape of the output samples
        to be drawn per alpha/beta-parameterized distribution.
      alpha: A Tensor or Python value or N-D array of type `dtype`. `alpha`
        provides the shape parameter(s) describing the gamma distribution(s) to
        sample. Must be broadcastable with `beta`.
      beta: A Tensor or Python value or N-D array of type `dtype`. Defaults to 1.
        `beta` provides the inverse scale parameter(s) of the gamma
        distribution(s) to sample. Must be broadcastable with `alpha`.
      dtype: The type of alpha, beta, and the output: `float16`, `float32`, or
        `float64`.
      seed: A Python integer. Used to create a random seed for the distributions.
        See
        [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
        for behavior.
      name: Optional name for the operation.
  
    Returns:
      samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))` with
        values of type `dtype`.
docstring_summary: Draws `shape` samples from each of the given Gamma distribution(s).
docstring_tokens: Draws shape samples from each of the given Gamma distribution ( s ) .
function: def random_gamma(shape,
                   alpha,
                   beta=None,
                   dtype=dtypes.float32,
                   seed=None,
                   name=None):
    """Draws `shape` samples from each of the given Gamma distribution(s).
  
    `alpha` is the shape parameter describing the distribution(s), and `beta` is
    the inverse scale parameter(s).
  
    Example:
  
      samples = tf.random_gamma([10], [0.5, 1.5])
      # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
      # the samples drawn from each distribution
  
      samples = tf.random_gamma([7, 5], [0.5, 1.5])
      # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
      # represents the 7x5 samples drawn from each of the two distributions
  
      samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]])
      # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.
  
    Args:
      shape: A 1-D integer Tensor or Python array. The shape of the output samples
        to be drawn per alpha/beta-parameterized distribution.
      alpha: A Tensor or Python value or N-D array of type `dtype`. `alpha`
        provides the shape parameter(s) describing the gamma distribution(s) to
        sample. Must be broadcastable with `beta`.
      beta: A Tensor or Python value or N-D array of type `dtype`. Defaults to 1.
        `beta` provides the inverse scale parameter(s) of the gamma
        distribution(s) to sample. Must be broadcastable with `alpha`.
      dtype: The type of alpha, beta, and the output: `float16`, `float32`, or
        `float64`.
      seed: A Python integer. Used to create a random seed for the distributions.
        See
        [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
        for behavior.
      name: Optional name for the operation.
  
    Returns:
      samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))` with
        values of type `dtype`.
    """
    with ops.op_scope([shape, alpha, beta], name, "random_gamma"):
      shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
      alpha = ops.convert_to_tensor(alpha, name="alpha", dtype=dtype)
      beta = ops.convert_to_tensor(beta if beta is not None else 1,
                                   name="beta",
                                   dtype=dtype)
      alpha_broadcast = alpha + array_ops.zeros_like(beta)
      seed1, seed2 = random_seed.get_seed(seed)
      return gen_random_ops._random_gamma(shape,
                                          alpha_broadcast,
                                          seed=seed1,
                                          seed2=seed2) / beta
function_tokens: def random_gamma ( shape , alpha , beta = None , dtype = dtypes . float32 , seed = None , name = None ) : with ops . op_scope ( [ shape , alpha , beta ] , name , "random_gamma" ) : shape = ops . convert_to_tensor ( shape , name = "shape" , dtype = dtypes . int32 ) alpha = ops . convert_to_tensor ( alpha , name = "alpha" , dtype = dtype ) beta = ops . convert_to_tensor ( beta if beta is not None else 1 , name = "beta" , dtype = dtype ) alpha_broadcast = alpha + array_ops . zeros_like ( beta ) seed1 , seed2 = random_seed . get_seed ( seed ) return gen_random_ops . _random_gamma ( shape , alpha_broadcast , seed = seed1 , seed2 = seed2 ) / beta