code: def test_shear_x(dtype):
      image = np.random.randint(low=0, high=255, size=(4, 4, 3)).astype(
          dtype.as_numpy_dtype
      )
      color = tf.constant([255, 0, 255], tf.int32)
      level = tf.random.uniform(shape=(), minval=0, maxval=1)
  
      tf_image = tf.constant(image)
      sheared_img = transform_ops.shear_x(
          tf_image, level, replace=color
      )
      transform_matrix = transform.AffineTransform(
          np.array([[1, level.numpy(), 0], [0, 1, 0], [0, 0, 1]])
      )
      if dtype == tf.uint8:
          # uint8 can't represent cval=-1, so we use int32 instead
          image = image.astype(np.int32)
      expected_img = transform.warp(
          image, transform_matrix, order=0, cval=-1, preserve_range=True
      )
  
      mask = np.where(expected_img == -1)
      expected_img[mask[0], mask[1], :] = color
  
      np.testing.assert_equal(sheared_img.numpy(), expected_img)
  
imports: import pytest
  import numpy
  import tensorflow
  from skimage import transform
  from tensorflow_addons.image import transform_ops
  from tensorflow_addons.utils import test_utils
label_desc: Use image node_id 21 for calling a global function with example usage: test_shear_x(dtype) without return types