tfhub_preprocess =  'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'
tfhub_encoder = 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/2'

def build_smallBERT_CNN_classifier_model():
    text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
    preprocessing = hub.KerasLayer(tfhub_preprocess, trainable=True, name='preprocessing')
    encoder_inputs = preprocessing(text_input)
    encoder = hub.KerasLayer(tfhub_encoder, trainable=True, name='BERT_encoder')
    outputs = encoder(encoder_inputs)
    net = sequence_output = outputs["sequence_output"]
    
    net = tf.keras.layers.Dense(64, activation="relu")(net)
    net = tf.keras.layers.Dropout(0.1)(net)
    
    net = tf.keras.layers.Dense(num_classes, activation="softmax", name='classifier')(net)
    
    return tf.keras.Model(text_input, net)

intent_classifier_model = build_smallBERT_CNN_classifier_model()

while running the above code I am facing the error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[27], line 1
----> 1 intent_classifier_model = build_smallBERT_CNN_classifier_model()

Cell In[26], line 4, in build_smallBERT_CNN_classifier_model()
      2 text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
      3 preprocessing = hub.KerasLayer(tfhub_preprocess, trainable=True, name='preprocessing')
----> 4 encoder_inputs = preprocessing(text_input)
      5 encoder = hub.KerasLayer(tfhub_encoder, trainable=True, name='BERT_encoder')
      6 outputs = encoder(encoder_inputs)

File /opt/conda/lib/python3.10/site-packages/tf_keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File /opt/conda/lib/python3.10/site-packages/tensorflow_hub/keras_layer.py:250, in KerasLayer.call(self, inputs, training)
    247   else:
    248     # Behave like BatchNormalization. (Dropout is different, b/181839368.)
    249     training = False
--> 250   result = smart_cond.smart_cond(training,
    251                                  lambda: f(training=True),
    252                                  lambda: f(training=False))
    254 # Unwrap dicts returned by signatures.
    255 if self._output_key:

File /opt/conda/lib/python3.10/site-packages/tensorflow_hub/keras_layer.py:252, in KerasLayer.call.<locals>.<lambda>()
    247   else:
    248     # Behave like BatchNormalization. (Dropout is different, b/181839368.)
    249     training = False
    250   result = smart_cond.smart_cond(training,
    251                                  lambda: f(training=True),
--> 252                                  lambda: f(training=False))
    254 # Unwrap dicts returned by signatures.
    255 if self._output_key:

File /opt/conda/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type.py:583, in canonicalize_to_monomorphic(args, kwargs, default_values, capture_types, polymorphic_type)
    577       parameters.append(
    578           _make_validated_mono_param(kwarg_name, arg[kwarg_name],
    579                                      Parameter.KEYWORD_ONLY, type_context,
    580                                      poly_parameter.type_constraint))
    581   else:
    582     parameters.append(
--> 583         _make_validated_mono_param(name, arg, poly_parameter.kind,
    584                                    type_context,
    585                                    poly_parameter.type_constraint))
    587 return FunctionType(parameters, capture_types), type_context

File /opt/conda/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type.py:522, in _make_validated_mono_param(name, value, kind, type_context, poly_type)
    518 def _make_validated_mono_param(
    519     name, value, kind, type_context, poly_type
    520 ) -> Parameter:
    521   """Generates and validates a parameter for Monomorphic FunctionType."""
--> 522   mono_type = trace_type.from_value(value, type_context)
    524   if poly_type and not mono_type.is_subtype_of(poly_type):
    525     raise TypeError(f"Parameter `{name}` was expected to be of type "
    526                     f"{poly_type} but is {mono_type}")

File /opt/conda/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py:185, in from_value(value, context)
    178   return default_types.Attrs.from_type_and_attributes(
    179       type(value),
    180       tuple(
    181           from_value(getattr(value, a.name), context)
    182           for a in value.__attrs_attrs__))
    184 if util.is_np_ndarray(value):
--> 185   ndarray = value.__array__()
    186   return default_types.TENSOR(ndarray.shape, ndarray.dtype)
    188 if isinstance(value, custom_nest_protocol.CustomNestProtocol):

File /opt/conda/lib/python3.10/site-packages/keras/src/backend/common/keras_tensor.py:61, in KerasTensor.__array__(self)
     60 def __array__(self):
---> 61     raise ValueError(
     62         "A KerasTensor is symbolic: it's a placeholder for a shape "
     63         "an a dtype. It doesn't have any actual numerical value. "
     64         "You cannot convert it to a NumPy array."
     65     )

ValueError: Exception encountered when calling layer 'preprocessing' (type KerasLayer).

A KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.

Call arguments received by layer 'preprocessing' (type KerasLayer):
  • inputs=<KerasTensor shape=(None,), dtype=string, sparse=None, name=text>

I am looking forward for the solution or the explanation, why this error is occuring?