code: def _parse_line(line):
      # Decode the line into its fields
      fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
  
      # Pack the result into a dictionary
      features = dict(zip(CSV_COLUMN_NAMES, fields))
  
      # Separate the label from the features
      label = features.pop("Species")
  
      return features, label
  
file_path: auptimizer/Examples/tf_iris_diff_opt/iris_data.py
imports: import pandas
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: _parse_line(line) and returns: features,  label