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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def build_csv_serving_tensors_for_training_step(analysis_path, features, schema, stats, keep_target):
 """Builds a serving function starting from raw csv, used at model export time. For image columns, the image should be a base64 string encoding the image. The output of this function will transform that image to a 2048 long vector using the inception model and then a fully connected net is attached to the 2048 long image embedding. """


  transformed_features, _, placeholder_dict = build_csv_serving_tensors_for_transform_step(
      analysis_path=analysis_path,
      features=features,
      schema=schema,
      stats=stats,
      keep_target=keep_target)

  transformed_features = image_feature_engineering(
      features=features,
      feature_tensors_dict=transformed_features)

  return input_fn_utils.InputFnOps(
      transformed_features, None, placeholder_dict)