diff --git a/smdebug/tensorflow/keras.py b/smdebug/tensorflow/keras.py
index 090b1676f..30ba008c9 100644
--- a/smdebug/tensorflow/keras.py
+++ b/smdebug/tensorflow/keras.py
@@ -556,11 +556,13 @@ def _save_layer_input_and_outputs(self):
                 if self._is_collection_being_saved_for_step(CollectionKeys.LAYERS)
                 else set()
             )
-            if hasattr(tensor, "numpy"):
-                self._save_tensor_to_file(export_name, tensor.numpy(), input_collection)
-            else:
+            t = tensor[0] if isinstance(tensor, list) and len(tensor) else tensor
+            if hasattr(t, "numpy") is False:
                 self.logger.warning("cannot save layer values during forward pass with tf.function")
                 continue
+            else:
+                self._save_tensor_to_file(export_name, tensor, input_collection)
+
             # Save Output
             tensor = self.saved_layers[layer_name].layer_output
             export_name = get_export_name_for_keras(layer_name, tensor_type="output", tensor=tensor)
@@ -570,8 +572,11 @@ def _save_layer_input_and_outputs(self):
                 if self._is_collection_being_saved_for_step(CollectionKeys.LAYERS)
                 else set()
             )
-            if hasattr(tensor, "numpy"):
-                self._save_tensor_to_file(export_name, tensor.numpy(), output_collection)
+            t = tensor[0] if isinstance(tensor, list) and len(tensor) else tensor
+            if hasattr(t, "numpy") is False:
+                self.logger.warning("cannot save layer values during forward pass with tf.function")
+            else:
+                self._save_tensor_to_file(export_name, tensor, output_collection)
 
     def _save_tensors_post_step(self, batch, logs):
         # some tensors available as value from within hook are saved here
diff --git a/tests/tensorflow2/test_concat_layer.py b/tests/tensorflow2/test_concat_layer.py
new file mode 100644
index 000000000..d7b31bd5b
--- /dev/null
+++ b/tests/tensorflow2/test_concat_layer.py
@@ -0,0 +1,38 @@
+# Third Party
+import numpy as np
+from tensorflow.keras.layers import Concatenate, Dense
+from tensorflow.python.keras.models import Model
+
+# First Party
+import smdebug.tensorflow as smd
+from smdebug.trials import create_trial
+
+
+class MyModel(Model):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self.con = Concatenate()
+        self.dense = Dense(10, activation="relu")
+
+    def call(self, x):
+        x = self.con([x, x])
+        return self.dense(x)
+
+
+def test_multiple_inputs(out_dir):
+    my_model = MyModel()
+    hook = smd.KerasHook(
+        out_dir, save_all=True, save_config=smd.SaveConfig(save_steps=[0], save_interval=1)
+    )
+
+    hook.register_model(my_model)
+    x_train = np.random.random((1000, 20))
+    y_train = np.random.random((1000, 1))
+    my_model.compile(optimizer="Adam", loss="mse", run_eagerly=True)
+    my_model.fit(x_train, y_train, epochs=1, steps_per_epoch=1, callbacks=[hook])
+
+    trial = create_trial(path=out_dir)
+    tnames = sorted(trial.tensor_names(collection=smd.CollectionKeys.LAYERS))
+    assert "concatenate" in tnames[0]
+    assert len(trial.tensor(tnames[0]).value(0)) == 2
+    assert trial.tensor(tnames[0]).shape(0) == (2, 1000, 20)