message: Some docfixes for random ops.
  - random_normal and random_uniform can take in broadcastable arguments of arbitrary rank. Remove the rank-1 restriction in the doc.
  - Change broadcasting in random_gamma to use broadcast_dynamic_shape and broadcast_to.
diff: @@ -51,10 +51,10 @@ def random_normal(shape,
  Args:
  shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
  - mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
  - distribution.
  - stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
  - of the normal distribution.
  + mean: A Tensor or Python value of type `dtype`, broadcastable with `stddev`.
  + The mean of the normal distribution.
  + stddev: A Tensor or Python value of type `dtype`, broadcastable with `mean`.
  + The standard deviation of the normal distribution.
  dtype: The type of the output.
  seed: A Python integer. Used to create a random seed for the distribution.
  See
  @@ -235,11 +235,12 @@ def random_uniform(shape,
  Args:
  shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
  - minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
  - range of random values to generate (inclusive). Defaults to 0.
  - maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on the
  - range of random values to generate (exclusive). Defaults to 1 if `dtype`
  - is floating point.
  + minval: A Tensor or Python value of type `dtype`, broadcastable with
  + `maxval`. The lower bound on the range of random values to generate
  + (inclusive). Defaults to 0.
  + maxval: A Tensor or Python value of type `dtype`, broadcastable with
  + `minval`. The upper bound on the range of random values to generate
  + (exclusive). Defaults to 1 if `dtype` is floating point.
  dtype: The type of the output: `float16`, `float32`, `float64`, `int32`,
  or `int64`.
  seed: A Python integer. Used in combination with `tf.random.set_seed` to
  @@ -513,7 +514,9 @@ def random_gamma(shape,
  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)
  + broadcast_shape = array_ops.broadcast_dynamic_shape(
  + array_ops.shape(alpha), array_ops.shape(beta))
  + alpha_broadcast = array_ops.broadcast_to(alpha, broadcast_shape)
  seed1, seed2 = random_seed.get_seed(seed)
  result = math_ops.maximum(
  np.finfo(alpha.dtype.as_numpy_dtype).tiny,
  