diff --git a/src/transformers/models/led/modeling_tf_led.py b/src/transformers/models/led/modeling_tf_led.py
index 0a803212d..bfd1954a8 100644
--- a/src/transformers/models/led/modeling_tf_led.py
+++ b/src/transformers/models/led/modeling_tf_led.py
@@ -19,6 +19,7 @@ import random
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
+import numpy as np
 import tensorflow as tf
 
 from ...activations_tf import get_tf_activation
@@ -26,6 +27,7 @@ from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions
 
 # Public API
 from ...modeling_tf_utils import (
+    TFModelInputType,
     TFPreTrainedModel,
     TFSharedEmbeddings,
     TFWrappedEmbeddings,
@@ -2390,23 +2392,23 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
     @replace_return_docstrings(output_type=TFLEDSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
-        input_ids=None,
-        attention_mask=None,
-        decoder_input_ids=None,
-        decoder_attention_mask=None,
-        head_mask=None,
-        decoder_head_mask=None,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_outputs: Optional[TFLEDEncoderBaseModelOutput] = None,
-        global_attention_mask=None,
-        past_key_values=None,
-        inputs_embeds=None,
-        decoder_inputs_embeds=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        labels=None,
-        training=False,
+        global_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[tf.Tensor] = None,
+        training: bool = False,
     ):
         """
         Returns: