code: def build_csv_serving_tensors_for_training_step(analysis_path,
                                                  features,
                                                  schema,
                                                  stats,
                                                  keep_target):
    
  
    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)
docs: 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.