ModuleNotFoundError and message: No module named 'pandas.core.indexes.numeric' error occurred during deploying the model with extension packages

# Create package extension with a "pip_zip" package file

meta_prop_pkg_extn = {
    client.package_extensions.ConfigurationMetaNames.NAME: "category_encoders-2.6.0",
    client.package_extensions.ConfigurationMetaNames.DESCRIPTION: "Category Encodder package extension",
    client.package_extensions.ConfigurationMetaNames.TYPE: "pip_zip"
}

pkg_extn_details = client.package_extensions.store(meta_props=meta_prop_pkg_extn, file_path="category_encoders-2.6.0.zip")
pkg_extn_uid = client.package_extensions.get_uid(pkg_extn_details)
pkg_extn_url = client.package_extensions.get_href(pkg_extn_details)

# Add extension package to the runtime environment

base_sw_spec_uid = client.software_specifications.get_uid_by_name("runtime-22.2-py3.10")

meta_prop_sw_spec = {
    client.software_specifications.ConfigurationMetaNames.NAME: "category_encoders-2.6.0",
    client.software_specifications.ConfigurationMetaNames.DESCRIPTION: "Software specification for category_encoders-2.6.0",
    client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: {"guid": base_sw_spec_uid}
}

sw_spec_details = client.software_specifications.store(meta_props=meta_prop_sw_spec)
sw_spec_uid = client.software_specifications.get_uid(sw_spec_details)
print(sw_spec_uid)

client.software_specifications.add_package_extension(sw_spec_uid, pkg_extn_uid)

# Promote model to the deployment space

model_metadata = {
            client.repository.ModelMetaNames.NAME: 'DRUG_RandomForestWithCategoryEncoder',
            client.repository.ModelMetaNames.TYPE: 'scikit-learn_1.1',
            client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_uid,
            client.repository.ModelMetaNames.INPUT_DATA_SCHEMA: {'type': 'struct', 'fields': [\
                                     {'nullable': False, 'metadata': {}, 'name': 'Age', 'type': 'integer'}, \
                                     {'nullable': False, 'metadata': {}, 'name': 'Sex', 'type': 'string'}, \
                                     {'nullable': False, 'metadata': {}, 'name': 'BP', 'type': 'string'}, \
                                     {'nullable': False, 'metadata': {}, 'name': 'Cholesterol', 'type': 'string'}, \
                                     {'nullable': False, 'metadata': {}, 'name': 'Na', 'type': 'double'}, \
                                     {'nullable': False, 'metadata': {}, 'name': 'K', 'type': 'double'}],'id': '1'}
}

published_model = client.repository.store_model(model=drug_randomforest_model, meta_props=model_metadata, training_data=X_train, training_target=y_train)
published_model

# Deploy the model
metadata = {
    client.deployments.ConfigurationMetaNames.NAME: "DRUG_RandomForestWithCategoryEncoderOnline_Deployment",
    client.deployments.ConfigurationMetaNames.ONLINE: {}
}

created_deployment = client.deployments.create(published_model_uid, meta_props=metadata)

### FOLLOWING ERROR OCCURRED ###

# WMLClientError: Deployment creation failed for deployment id: 9ede2602-1cd3-4c6d-b998-6b7c28ab06e2. 
# Errors: [{'code': 'd776f8a7d40a5ce8b42cd365cf562207', 'message': "Model loading 
# through joblib failed with exception: ModuleNotFoundError 
# and message: No module named 'pandas.core.indexes.numeric'. The model object 
# provided for deployment is either invalid or trained using a version of scikit-learn other than: 1.3"}]