code: def preprocess(passengers, columns_to_delete):
      # Sort by descending id and delete columns
      for column_to_delete in sorted(columns_to_delete, reverse=True):
          [passenger.pop(column_to_delete) for passenger in passengers]
      for i in range(len(passengers)):
          # Converting 'sex' field to float (id is 1 after removing labels column)
          passengers[i][1] = (
              1.0 if passengers[i][1] == "female" else 0.0
          )
      return np.array(passengers, dtype=np.float32)
  
imports: from __future__ import print_function
  import numpy
  import tflearn
  from tflearn.datasets import titanic
  from tflearn.data_utils import load_csv
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess(passengers, columns_to_delete) and returns: np