title: UnimplementedError: File system scheme '[local]' not implemented
body: <p>I am getting an error while implementing TensorFlow in TPU</p>
  <pre><code>UnimplementedError: File system scheme '[local]' not implemented (file: '1.png')
  </code></pre>
  <p>I know this question has been answered <a href="https://stackoverflow.com/questions/62870656/file-system-scheme-local-not-implemented-in-google-colab-tpu">before</a> but my issue is different,
  I am getting this error when I do</p>
  <pre><code>for i, j in train_dataset.take(3):
      print(i,j)
  </code></pre>
  <p>It works with <code>train_dataset.take(3)</code></p>
  <p>Here are my functions</p>
  <pre><code>def decode(img,image_size=(IMG_SIZE, IMG_SIZE)):
      bits = tf.io.read_file(img)
      image = tf.image.decode_jpeg(bits, channels=3)
      image = tf.cast(image, tf.float32) / 255.0
      image = tf.image.resize(image, image_size)
      
      image = tf.image.random_flip_left_right(image, seed=2020)
      image = tf.image.random_flip_up_down(image, seed=2020)
      image = tf.image.random_crop(image,size=[IMG_SIZE,IMG_SIZE,3],seed=2020 )
      image = tf.image.random_brightness(image,max_delta=0.5 )
      image = tf.image.rot90(image)
      return image
  
  def decode_image(img,labels=None ):
      if labels is None:
          return decode(img)
      else:
          return decode(img),labels 
  
  train_image=tf.data.Dataset.from_tensor_slices((train.iloc[:,0],train.iloc[:,1::] ))
  train_dataset=train_image.map(decode_image, num_parallel_calls=AUTO).repeat().shuffle(512).batch(BATCH_SIZE).prefetch(AUTO)
  test_image=tf.data.Dataset.from_tensor_slices((test.iloc[:,0]))
  test_dataset=test_image.map(decode_image, num_parallel_calls=AUTO).batch(BATCH_SIZE)
  </code></pre>
  <p>How should I resolve it?</p>
  <p>It might an issue with the path. SO I am adding how is set path
  This is how the directory looks like</p>
  <pre><code>weights
  images
  -train
  --train
  ---train
  ----img1
  ----img2
  ---csv
  -val
  --val
  ---img1
  </code></pre>
  <p>When I run</p>
  <pre><code>GCS_DS_PATH = KaggleDatasets().get_gcs_path('images')
  !gsutil ls $GCS_DS_PATH
  </code></pre>
  <p>I got following</p>
  <pre><code>gs://kds-aab923e1c9bc934f088881f1e537365b8f18fe192b3b3dc14e272a37/train/
  gs://kds-aab923e1c9bc934f088881f1e537365b8f18fe192b3b3dc14e272a37/val/
  </code></pre>
  <p>This is how my paths are set</p>
  <pre><code>def train_format_path(st):
      return GCS_DS_PATH + '/train/train/train/' + st 
  
  def test_format_path(st):
      return GCS_DS_PATH + '/val/val/' + st 
  
  train_paths = train.ID.apply(train_format_path).values
  test_paths = test.ID.apply(test_format_path).values
  </code></pre>
  <p>With <code>train_paths[0]</code>
  I got</p>
  <pre><code>'gs://kds-aab923e1c9bc934f088881f1e537365b8f18fe192b3b3dc14e272a37/train/train/train/1.png'
  </code></pre>