When fine tune the ConvMixer with tf.hub has ERROR:absl:hub.KerasLayer is trainable but has zero trainable weights.

[vscv]

Describe the bug
When fine tune the ConvMixer with tf.hub has ERROR:absl:hub.KerasLayer is trainable but has zero trainable weights.

To Reproduce
Steps to reproduce the behavior:

Tensorflow 2.6.0
`
resize_input = resize_layer_224(inputs)
rescaling_input = scale_layer_2(resize_input)

base_model = hub.KerasLayer(https://tfhub.dev/rishit-dagli/convmixer-1536-20-fe/1, name = CVMixer, trainable=True)

b_m_output = base_model(rescaling_input)
reshape_output = tf.keras.layers.Reshape((1024,))(b_m_output)
outputs = tf.keras.layers.Dense(outputnum, activation="softmax", name="top_output")(reshape_output)

model = tf.keras.Model(inputs=inputs, outputs=outputs, name=model_name)
model.compile(
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001),
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['accuracy']
)
`

`
ERROR:absl:hub.KerasLayer is trainable but has zero trainable weights.

Traceback (most recent call last):

File "/home/.py", line 1009, in build_tf_hub_models
b_m_output = base_model(rescaling_input)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 976, in call
return self._functional_construction_call(inputs, args, kwargs,
File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1114, in _functional_construction_call
outputs = self._keras_tensor_symbolic_call(
File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 848, in _keras_tensor_symbolic_call
return self._infer_output_signature(inputs, args, kwargs, input_masks)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 888, in _infer_output_signature
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py", line 695, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

/home/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py:229 call  *
    result = f()
/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py:664 _call_attribute  **
    return instance.__call__(*args, **kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py:885 __call__
    result = self._call(*args, **kwds)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py:933 _call
    self._initialize(args, kwds, add_initializers_to=initializers)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py:759 _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py:3066 _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py:3463 _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py:3298 _create_graph_function
    func_graph_module.func_graph_from_py_func(
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py:1007 func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py:668 wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_deserialization.py:288 restored_function_body
    raise ValueError(

ValueError: Could not find matching function to call loaded from the SavedModel. Got:
  Positional arguments (1 total):
    * Tensor("None_0:0", shape=(None, 224, 224, 3), dtype=float32)
  Keyword arguments: {}

Expected these arguments to match one of the following 1 option(s):

Option 1:
  Positional arguments (0 total):
    * 
  Keyword arguments: {'input': TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input')}`

Expected behavior
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