new_contents: import tensorflow as tf
  import numpy as np
  #import pandas as pd 
  
  def getdataset(myobj):
      if myobj.dataset == 'mnist':
          return getmnist()
      if myobj.dataset == 'dailymintemperatures':
          return getdailymintemperatures(myobj)
  
  def getmnist():
      #load mnist data
      (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data("/tmp/datasets/mnist.npz")
      def create_mnist_dataset(data, labels, batch_size):
          def gen():
              for image, label in zip(data, labels):
                  yield image, label
          ds = tf.data.Dataset.from_generator(gen, (tf.float32, tf.int32), ((28,28 ), ()))
          return ds.repeat().batch(batch_size)
      #train and validation dataset with different batch size
      train_dataset = create_mnist_dataset(x_train, y_train, 10)
      valid_dataset = create_mnist_dataset(x_test, y_test, 20)
      print(type(x_train))
      print(type(y_train))
      print(type(train_dataset))
      x_train = x_train.reshape((x_train.shape[0], 784))
      x_test = x_test.reshape((x_test.shape[0], 784))
      y_train = np.int32(y_train)
      y_test = np.int32(y_test)
      print(x_train.shape)
      print(y_train.shape)
      return x_train, y_train, x_test, y_test, 784, 10
  
  def getdailymintemperatures(myobj):
      url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv'
      file_path = tf.keras.utils.get_file("/tmp/daily-min-temperatures.csv", url)
      data = np.genfromtxt(file_path, delimiter = ',', skip_header = 1, dtype = {'names': ('date', 'temp'), 'formats': (np.str, np.float)})
      data = data['temp']
      return data, None, None, None, myobj.size, myobj.classes
      
  
subject: Add tests for neural net (I106).