code: def load_csv(
      filepath,
      target_column=-1,
      columns_to_ignore=None,
      has_header=True,
      categorical_labels=False,
      n_classes=None,
  ):
      """load_csv.
  
      Load data from a CSV file. By default the labels are considered to be the
      last column, but it can be changed by filling 'target_column' parameter.
  
      Arguments:
          filepath: `str`. The csv file path.
          target_column: The id of the column representing the labels.
              Default: -1 (The last column).
          columns_to_ignore: `list of int`. A list of columns index to ignore.
          has_header: `bool`. Whether the csv file has a header or not.
          categorical_labels: `bool`. If True, labels are returned as binary
              vectors (to be used with 'categorical_crossentropy').
          n_classes: `int`. Total number of class (needed if
              categorical_labels is True).
  
      Returns:
          A tuple (data, target).
  
      """
  
      from tensorflow.python.platform import gfile
  
      with gfile.Open(filepath) as csv_file:
          data_file = csv.reader(csv_file)
          if not columns_to_ignore:
              columns_to_ignore = []
          if has_header:
              header = next(data_file)
          data, target = [], []
          # Fix column to ignore ids after removing target_column
          for i, c in enumerate(columns_to_ignore):
              if c > target_column:
                  columns_to_ignore[i] -= 1
          for i, d in enumerate(data_file):
              target.append(d.pop(target_column))
              data.append(
                  [
                      _d
                      for j, _d in enumerate(d)
                      if j not in columns_to_ignore
                  ]
              )
          if categorical_labels:
              assert isinstance(
                  n_classes, int
              ), "n_classes not specified!"
              target = to_categorical(target, n_classes)
          return data, target
  
imports: from __future__ import division, print_function, absolute_import
  import os
  import random
  import numpy
  from PIL import Image
  import pickle
  import csv
  import warnings
  import tensorflow.compat.v1
  from io import BytesIO
label_desc: Use image node_id 25 for calling a global function with example usage: load_csv(filepath, target_column, columns_to_ignore, has_header, categorical_labels, n_classes) and returns: data,  target