fix F811

keras/mixed_precision/loss_scale_optimizer_test.py

@@ -385,8 +385,17 @@ def testClipping(self, opt_cls, strategy_fn, use_tf_function):
                 self.assertEqual(self.evaluate(opt.loss_scale), 8)
 
                 # Test Inf gradients are still skipped instead of being clipped
+<<<<<<< HEAD
                 loss = lambda: var * float("Inf")
                 run_fn = lambda: opt.minimize(loss, var_list=[var])
+=======
+                def run_fn():
+                    def loss():
+                        return var * float("Inf")
+
+                    return opt.minimize(loss, var_list=[var])
+
+>>>>>>> 0bb24689 (fix F811)
                 run_op = strategy.experimental_run(run_fn)
                 self._run_if_in_graph_mode(run_op)
                 self.assertAllClose(
@@ -417,8 +426,17 @@ def testDynamicUpdate(self, opt_cls, strategy_fn, use_tf_function):
             self.assertEqual(4.0, self.evaluate(opt.loss_scale))
 
             # Test optimizer with NaN gradients
+<<<<<<< HEAD
             loss = lambda: var * float("NaN")
             run_fn = lambda: opt.minimize(loss, var_list=[var])
+=======
+            def run_fn():
+                def loss():
+                    return var * float("NaN")
+
+                return opt.minimize(loss, var_list=[var])
+
+>>>>>>> 0bb24689 (fix F811)
             run_op = strategy.experimental_run(run_fn)
             self._run_if_in_graph_mode(run_op)
             # Variable should not change from before, due to NaN gradients.

keras/tests/keras_doctest.py

@@ -22,7 +22,6 @@
 import sys
 
 import numpy as np
-import tensorflow as tf
 import tensorflow.compat.v2 as tf
 from absl import flags
 from absl.testing import absltest