code: def run(input_data):
      logger = EntryScript().logger
      logger.info("run() is called with: {}.".format(input_data))
  
      # make inference
      num_rows, num_cols = input_data.shape
      pred = iris_model.predict(input_data).reshape((num_rows, 1))
  
      # cleanup output
      result = input_data.drop(input_data.columns[4:], axis=1)
      result["variety"] = pred
  
      return result
  
file_path: MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/Code/iris_score.py
imports: import io
  import pickle
  import argparse
  import numpy
  from azureml.core.model import Model
  from sklearn.linear_model import LogisticRegression
  from azureml_user.parallel_run import EntryScript
label_desc: Use image node_id 2 for calling a global function with example usage: run(input_data) and returns: result