Fixed documentation of Optimizer.minimize() for eager mode to match behavior of Optimizer.compute_gradients().

PiperOrigin-RevId: 213060585

1 files changed, 6 insertions, 7 deletions

diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 2304a461c1..699162b30c 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -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(