from tensorflow.python.ops import parsing_ops

  image = image_ops.resize_bilinear(image, [height, width])
  return array_ops.squeeze(image, [0])


def _create_tfrecord_dataset(tmpdir):
  if not gfile.Exists(tmpdir):
    gfile.MakeDirs(tmpdir)

  data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)

  keys_to_features = {
      'image/encoded':
          parsing_ops.FixedLenFeature(
              shape=(), dtype=dtypes.string, default_value=''),
      'image/format':
          parsing_ops.FixedLenFeature(
              shape=(), dtype=dtypes.string, default_value='jpeg'),
      'image/class/label':
          parsing_ops.FixedLenFeature(
              shape=[1],
              dtype=dtypes.int64,
              default_value=array_ops.zeros(
                  [1], dtype=dtypes.int64))
  }

  items_to_handlers = {
      'image': tfexample_decoder.Image(),
      'label': tfexample_decoder.Tensor('image/class/label'),
  }

