code: def test_images_float(self, make_lib_fail, dtype):
      with make_lib_fail():
          img = np.random.rand(28, 28, 1).astype(np.float32)
          img_other_shape = np.random.rand(12, 34, 1).astype(np.float32)
  
          numpy_array = testing.FeatureExpectationItem(
              value=img,
              expected=img,
              expected_np=img,
          )
          img_shape_can_be_dynamic = testing.FeatureExpectationItem(
              value=img_other_shape,
              expected=img_other_shape,
              expected_np=img_other_shape,
          )
          invalid_type = testing.FeatureExpectationItem(
              value=img.astype(np.float64),
              raise_cls=ValueError,
              raise_cls_np=ValueError,
              raise_msg="dtype should be",
          )
          self.assertFeature(
              feature=features_lib.Image(
                  shape=(None, None, 1), dtype=dtype
              ),
              shape=(None, None, 1),
              dtype=dtype,
              tests=[
                  numpy_array,
                  img_shape_can_be_dynamic,
                  invalid_type,
              ],
              test_attributes=dict(
                  _encoding_format=None,
                  _use_colormap=False,
              ),
          )
  
imports: import contextlib
  import enum
  import os
  import pathlib
  from typing import Optional, Type
  from absl.testing import parameterized
  import numpy
  import PIL
  import pytest
  import tensorflow
  import tensorflow_datasets
  from tensorflow_datasets import testing
  from tensorflow_datasets.core import features
label_desc: Use image node_id 4 for calling the ImageFeatureTest obj's underlying member method code with example usage: obj.test_images_float(make_lib_fail, dtype) without return types