diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py
index aeeb9d3d5a8822..a6d566dc551fbe 100644
--- a/src/transformers/modeling_tf_utils.py
+++ b/src/transformers/modeling_tf_utils.py
@@ -57,6 +57,7 @@
     is_offline_mode,
     is_remote_url,
     is_safetensors_available,
+    is_tf_symbolic_tensor,
     logging,
     requires_backends,
     working_or_temp_dir,
@@ -511,7 +512,7 @@ def input_processing(func, config, **kwargs):
     if isinstance(main_input, (tuple, list)):
         for i, input in enumerate(main_input):
             # EagerTensors don't allow to use the .name property so we check for a real Tensor
-            if type(input) == tf.Tensor:
+            if is_tf_symbolic_tensor(input):
                 # Tensor names have always the pattern `name:id` then we check only the
                 # `name` part
                 tensor_name = input.name.split(":")[0]
@@ -572,7 +573,7 @@ def input_processing(func, config, **kwargs):
     # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
     # So to respect the proper output we have to add this exception
     if "args" in output:
-        if output["args"] is not None and type(output["args"]) == tf.Tensor:
+        if output["args"] is not None and is_tf_symbolic_tensor(output["args"]):
             tensor_name = output["args"].name.split(":")[0]
             output[tensor_name] = output["args"]
         else:
diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py
index 003b29a2db23f9..c79a09a94091d7 100644
--- a/src/transformers/utils/__init__.py
+++ b/src/transformers/utils/__init__.py
@@ -43,6 +43,7 @@
     is_numpy_array,
     is_tensor,
     is_tf_tensor,
+    is_tf_symbolic_tensor,
     is_torch_device,
     is_torch_dtype,
     is_torch_tensor,
diff --git a/src/transformers/utils/generic.py b/src/transformers/utils/generic.py
index 9c620fcc0db2c1..43571e9e896905 100644
--- a/src/transformers/utils/generic.py
+++ b/src/transformers/utils/generic.py
@@ -166,6 +166,22 @@ def is_tf_tensor(x):
     return False if not is_tf_available() else _is_tensorflow(x)
 
 
+def _is_tf_symbolic_tensor(x):
+    import tensorflow as tf
+
+    # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
+    if hasattr(tf, 'is_symbolic_tensor'):
+        return tf.is_symbolic_tensor(x)
+    return type(x) == tf.Tensor
+
+
+def is_tf_symbolic_tensor(x):
+    """
+    Tests if `x` is a tensorflow symbolic tensor or not (ie. not eager). Safe to call even if tensorflow is not installed.
+    """
+    return False if not is_tf_available() else _is_tf_symbolic_tensor(x)
+
+
 def _is_jax(x):
     import jax.numpy as jnp  # noqa: F811