code: def _pad_masks_tf(
      masks, crop_box: List[int], orig_height: int, orig_width: int
  ):
      left, top, right, bottom = crop_box
      if (
          left == 0
          and top == 0
          and right == orig_width
          and bottom == orig_height
      ):
          return masks
      # Coordinate transform masks
      pad_x, pad_y = orig_width - (right - left), orig_height - (
          bottom - top
      )
      pad = (left, pad_x - left, top, pad_y - top)
      return tf.pad(masks, pad, constant_values=0)
  
imports: import math
  from copy import deepcopy
  from itertools import product
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import convert_to_rgb, pad, resize, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from ...utils import TensorType, is_tf_available, is_torch_available, is_torchvision_available, logging, requires_backends
label_desc: Use image node_id 9 for calling a global function with example usage: _pad_masks_tf(masks, crop_box, orig_height, orig_width) and returns: tf, masks