code: def _preprocessing_fn(inputs):
      """Preprocess input columns into transformed columns.
  
      Args:
        inputs: Input Tensors.
  
      Returns:
       Dictionary of respective transformed inputs
  
      Example:
        `int_features`: tft.scale_to_0_1(...)
          Input:    [[[0]],   [[1]], [[2]]]
          Output:   [[[0]], [[0.5]], [[1]]]
  
        `float_features`: tft.scale_to_0_1(.., elementwise = True)
          Input:  [
              [[1.0, 2.0, 3.0, 4.0]],
              [[2.0, 3.0, 4.0, 5.0]],
              [[3.0, 4.0, 0.0, 0.0]]
          ]
          Output: [
              [[0.0, 0.0, 0.75, 0.8]],
              [[0.5, 0.5,  1.0, 1.0]],
              [[1.0, 1.0,  0.0, 0.0]]
          ]
  
        `seq_int_feature`: tft.scale_by_min_max(...)
          Input: [
              [ [1, 2],   [3, 4]],
              [ [5, 6],   [7, 8]],
              [[9, 10], [11, 12]]
          ]
          Output: [
              [[   0.0, 0.0909], [0.1818, 0.2727]],
              [[0.3636, 0.4545], [0.5454, 0.6363]],
              [[0.7272, 0.8181], [0.9090,    1.0]]
          ]
  
        `seq_string_feature`: tft.compute_and_apply_vocabulary(...)
          Input: [
              [[ b'Hello', b'World'], []],
              [[   b'foo',   b'bar'], []],
              [[b'tensor',  b'flow'], []]
          ]
          Output: [
              [[[5, 4], []]],
              [[[1, 3], []]],
              [[[0, 2], []]]
          ]
      """
      return {
          "transformed_seq_int_feature": tft.scale_by_min_max(
              inputs["seq_int_feature"]
          ),
          "transformed_seq_string_feature": tft.compute_and_apply_vocabulary(
              inputs["seq_string_feature"]
          ),
          "transformed_float_feature": tft.scale_to_0_1(
              inputs["float_feature"], elementwise=True
          ),
          "transformed_int_feature": tft.scale_to_0_1(
              inputs["int_feature"]
          ),
      }
  
file_path: transform/examples/simple_sequence_example.py
imports: import os
  import tempfile
  from absl import logging
  import apache_beam
  import tensorflow_transform
  import tensorflow_transform.beam
  from tfx_bsl.public import tfxio
  from tensorflow_metadata.proto.v0 import schema_pb2
  from google.protobuf import text_format
label_desc: Use image node_id 3 for calling a global function with example usage: _preprocessing_fn(inputs) and returns: dict