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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _distort_color(image, color_ordering=0, scope=None):
 """Distort the color of a Tensor image. Each color distortion is non-commutative and thus ordering of the color ops matters. Ideally we would randomly permute the ordering of the color ops. Rather then adding that level of complication, we select a distinct ordering of color ops for each preprocessing thread. Args: image: 3-D Tensor containing single image in [0, 1]. color_ordering: Python int, a type of distortion (valid values: 0-3). scope: Optional scope for name_scope. Returns: 3-D Tensor color-distorted image on range [0, 1] Raises: ValueError: if color_ordering not in [0, 3] """

  with tf.name_scope(scope, "distort_color", [image]):
    if color_ordering == 0:
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
      image = tf.image.random_hue(image, max_delta=0.2)
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
    elif color_ordering == 1:
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
      image = tf.image.random_hue(image, max_delta=0.2)
    elif color_ordering == 2:
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
      image = tf.image.random_hue(image, max_delta=0.2)
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
    elif color_ordering == 3:
      image = tf.image.random_hue(image, max_delta=0.2)
      image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
      image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
      image = tf.image.random_brightness(image, max_delta=32. / 255.)
    else:
      raise ValueError("color_ordering must be in [0, 3]")

    # The random_* ops do not necessarily clamp.
    return tf.clip_by_value(image, 0.0, 1.0)