df = pd.read_csv("dataset")
X_train, X_test, y_train, y_test =
train_test_split(df.iloc[:, :-1].values, df.iloc[:, -1].values)
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
batch_size = 64
with tf.Session() as sess:
  dataset = tf.data.Dataset.from_tensor_slices((X_train_scaled, y_train))
  dataset = dataset.cache()
  dataset = dataset.shuffle(len(X_train_scaled))
  dataset = dataset.repeat()
  dataset = dataset.batch(batch_size)
  dataset = dataset.prefetch(batch_size*10)
  iterator = dataset.make_one_shot_iterator()
  print(sess.run(iterator.get_next()[0]),sess.run(iterator.get_next()[1]))
  his = model.fit(dataset, epochs=300, steps_per_epoch=1000, verbose=0)

AttributeError: 'PrefetchDataset' object has no attribute 'ndim'