message: Resolving pylint
diff: @@ -170,7 +170,6 @@ def custom_gradient(f=None):
  require special handling because they are effectively inputs of the forward
  function.
  - ```python
  >>> weights = tf.Variable(tf.ones([2])) # Trainable variable weights
  >>> @tf.custom_gradient
  ... def linear_poly(x):
  @@ -206,7 +205,6 @@ def custom_gradient(f=None):
  numpy=array([1., 1., 1.], dtype=float32)>
  >>> tape.gradient(poly, weights)
  <tf.Tensor: shape=(2,), dtype=float32, numpy=array([6., 3.], dtype=float32)>
  - ```
  Above example illustrates usage of trainable variable `weights`.
  In the example, the inner `grad_fn` accepts an extra `variables` input
  