message: Fixed documentation of Optimizer.minimize() for eager mode to match behavior of Optimizer.compute_gradients().
diff: @@ -385,13 +385,12 @@ class Optimizer(
  @compatibility(eager)
  When eager execution is enabled, `loss` should be a Python function that
  - takes elements of `var_list` as arguments and computes the value to be
  - minimized. If `var_list` is None, `loss` should take no arguments.
  - Minimization (and gradient computation) is done with respect to the
  - elements of `var_list` if not None, else with respect to any trainable
  - variables created during the execution of the `loss` function.
  - `gate_gradients`, `aggregation_method`, `colocate_gradients_with_ops` and
  - `grad_loss` are ignored when eager execution is enabled.
  + takes no arguments and computes the value to be minimized. Minimization (and
  + gradient computation) is done with respect to the elements of `var_list` if
  + not None, else with respect to any trainable variables created during the
  + execution of the `loss` function. `gate_gradients`, `aggregation_method`,
  + `colocate_gradients_with_ops` and `grad_loss` are ignored when eager
  + execution is enabled.
  @end_compatibility
  """
  grads_and_vars = self.compute_gradients(
  