More TF int dtype fixes (#20384)

* Add a test to ensure int dummy inputs are int64

* Move the test into the existing int64 test and update a lot of existing dummies

* Fix remaining dummies

* Fix remaining dummies

* Test for int64 serving sigs as well

* Update core tests to use tf.int64

* Add better messages to the assertions

* Update all serving sigs to int64

* More sneaky hiding tf.int32s

* Add an optional int32 signature in save_pretrained

* make fixup

* Add Amy's suggestions

* Switch all serving sigs back to tf.int32

* Switch all dummies to tf.int32

* Adjust tests to check for tf.int32 instead of tf.int64

* Fix base dummy_inputs dtype

* Start casting to tf.int32 in input_processing

* Change dtype for unpack_inputs test

* Add proper tf.int32 test

* Make the alternate serving signature int64

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -821,7 +821,7 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
         Returns:
             `Dict[str, tf.Tensor]`: The dummy inputs.
         """
-        dummy = {"input_ids": tf.constant(DUMMY_INPUTS)}
+        dummy = {"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int64)}
         # Add `encoder_hidden_states` to make the cross-attention layers' weights initialized
         if self.config.add_cross_attention:
             batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape
@@ -1365,7 +1365,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
         Returns:
             tf.Tensor with dummy inputs
         """
-        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
+        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int64)}
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))