message: Prefer non-nested GradientTape.gradient call when only one source is passed.
diff: @@ -227,8 +227,8 @@ w = tfe.Variable([[1.0]])
  with tf.GradientTape() as tape:
  loss = w * w
  -grad = tape.gradient(loss, [w])
  -print(grad) # => [tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)]
  +grad = tape.gradient(loss, w)
  +print(grad) # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)
  ```
  Here's an example of `tf.GradientTape` that records forward-pass operations
  @@ -596,7 +596,7 @@ def line_search_step(fn, init_x, rate=1.0):
  # Variables are automatically recorded, but manually watch a tensor
  tape.watch(init_x)
  value = fn(init_x)
  - grad, = tape.gradient(value, [init_x])
  + grad = tape.gradient(value, init_x)
  grad_norm = tf.reduce_sum(grad * grad)
  init_value = value
  while value > init_value - rate * grad_norm:
  