code: def get_transformed_images(
      x: np.ndarray,
      mask: np.ndarray,
      xforms: List[
          Tuple[
              float,
              float,
              float,
              int,
              float,
              float,
              float,
              float,
              float,
          ]
      ],
      lbd: float,
      theta: np.ndarray,
      net_size: Tuple[int, int],
      clip_min: float,
      clip_max: float,
      pts: Optional[np.ndarray] = None,
  ) -> List[np.ndarray]:
      """
      Get transformed images.
  
      :param x: Input image.
      :param mask: Mask image.
      :param xforms: Transformation parameters.
      :param lbd: lambda multiplier for the perturbation.
      :param theta: The perturbation.
      :param net_size: Size of the image for the network.
      :param clip_min: Minimum value of an example.
      :param clip_max: Maximum value of an example.
      :param pts: A set of points that will set the crop size in the perspective transform.
      :return: List of transformed images.
      """
      att, pert, mask = add_noise(x, mask, lbd, theta, True)
  
      if len(xforms) == 0:
          return [convert_to_network(att, net_size, clip_min, clip_max)]
  
      images = []
      for xform in xforms:
          (
              angle,
              dist,
              gamma,
              blur,
              crop_percent,
              crop_off_x,
              crop_off_y,
              obj_width,
              focal,
          ) = xform
          images.append(
              apply_transformation(
                  x,
                  mask,
                  pert,
                  angle,
                  dist,
                  gamma,
                  blur,
                  crop_percent,
                  crop_off_x,
                  crop_off_y,
                  net_size,
                  obj_width,
                  focal,
                  clip_min,
                  clip_max,
                  pts,
              )
          )
  
      return images
  
imports: from typing import Optional, Tuple, Union, TYPE_CHECKING, List
  import math
  import numpy
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification import ClassifierMixin
  from art.utils import get_labels_np_array
label_desc: Use image node_id 6 for calling a global function with example usage: get_transformed_images(x, mask, xforms, lbd, theta, net_size, clip_min, clip_max, pts) and returns: images, list