code: def preprocessing_fn(inputs):
      """tf.transform's callback function for preprocessing inputs.
  
      Args:
        inputs: map from feature keys to raw not-yet-transformed features.
  
      Returns:
        Map from string feature key to transformed feature operations.
      """
      outputs = {}
  
      # This function is the entry point for your feature engineering with
      # TensorFlow Transform, using the TFX Transform component.  In this example
      # the feature engineering is very simple, only applying z-score scaling.
      for key in features.FEATURE_KEYS:
          outputs[
              features.transformed_name(key)
          ] = tft.scale_to_z_score(inputs[key])
  
      # Do not apply label transformation as it will result in wrong evaluation.
      outputs[features.transformed_name(features.LABEL_KEY)] = inputs[
          features.LABEL_KEY
      ]
  
      return outputs
  
label_desc: Use image node_id 1 for calling a global function with example usage: preprocessing_fn(inputs) and returns: outputs