code: def load_fashion_mnist(
      input_rows,
      input_cols,
      path="./data/fashion/fashion-mnist_train.csv",
  ):
      # read the csv data
      df = pd.read_csv(path)
      # extract the image pixels
      X_train = df.drop(columns=["label"])
      X_train = X_train.values
      X_train = (X_train.astype("float32") - 127.5) / 127.5
      X_train = X_train.reshape(
          X_train.shape[0], input_rows, input_cols, 1
      )
      # extract the labels
      y_train = df["label"].values
  
      return X_train, y_train
  
imports: import pickle
  import os
  from keras.datasets import mnist, cifar100, cifar10
  from keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array
  import pandas
  import numpy
  from os import walk, getcwd
  import h5py
  import scipy
  from glob import glob
  from keras.applications import vgg19
  from keras import backend
  from keras.utils import to_categorical
  import pdb
label_desc: Use image node_id 4 for calling a global function with example usage: load_fashion_mnist(input_rows, input_cols, path) and returns: X_train,  y_train