code: def mnist():
      """Note that this dataset, unlike other Talos datasets,returns:
  
      x_train, y_train, x_val, y_val"""
  
      import tensorflow as tf
      import numpy as np
  
      # the data, split between train and test sets
      (x_train, y_train), (
          x_val,
          y_val,
      ) = tf.keras.datasets.mnist.load_data()
  
      # input image dimensions
      img_rows, img_cols = 28, 28
  
      if tf.keras.backend.image_data_format() == "channels_first":
          x_train = x_train.reshape(
              x_train.shape[0], 1, img_rows, img_cols
          )
          x_val = x_val.reshape(x_val.shape[0], 1, img_rows, img_cols)
          input_shape = (1, img_rows, img_cols)
  
      else:
          x_train = x_train.reshape(
              x_train.shape[0], img_rows, img_cols, 1
          )
          x_val = x_val.reshape(x_val.shape[0], img_rows, img_cols, 1)
          input_shape = (img_rows, img_cols, 1)
  
      x_train = x_train.astype("float32")
      x_val = x_val.astype("float32")
      x_train /= 255
      x_val /= 255
  
      classes = len(np.unique(y_train))
  
      # convert class vectors to binary class matrices
      y_train = tf.keras.utils.to_categorical(y_train, classes)
      y_val = tf.keras.utils.to_categorical(y_val, classes)
  
      print("Use input_shape %s" % str(input_shape))
  
      return x_train, y_train, x_val, y_val
  
label_desc: Use image node_id 7 for calling a global function with example usage: mnist() and returns: x_train,  y_train,  x_val,  y_val