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 = {}
  
      for key in FEATURE_KEYS:
          # tft.scale_to_z_score computes the mean and variance of the given feature
          # and scales the output based on the result.
          outputs[transformed_name(key)] = tft.scale_to_z_score(
              inputs[key]
          )
      # TODO(b/157064428): Support label transformation for Keras.
      # Do not apply label transformation as it will result in wrong evaluation.
      outputs[transformed_name(_LABEL_KEY)] = inputs[_LABEL_KEY]
  
      return outputs
  
file_path: tfx/tfx/examples/penguin/penguin_utils_base.py
imports: from typing import List
  from absl import logging
  import tensorflow
  import tensorflow_transform
  from tfx import v1
  from tfx_bsl.public import tfxio
label_desc: Use image node_id 4 for calling a global function with example usage: preprocessing_fn(inputs) and returns: outputs