code: def center_crop_generator(
      batch_size: int,
      height: int,
      width: int,
      size: Tuple[int, int],
      device: Device = torch.device("cpu"),
  ) -> Dict[str, Tensor]:
      r"""Get parameters for ```center_crop``` transformation for center crop transform.
  
      Args:
          batch_size (int): the tensor batch size.
          height (int) : height of the image.
          width (int): width of the image.
          size (tuple): Desired output size of the crop, like (h, w).
          device (Device): the device on which the random numbers will be generated. Default: cpu.
  
      Returns:
          params Dict[str, Tensor]: parameters to be passed for transformation.
              - src (Tensor): cropping bounding boxes with a shape of (B, 4, 2).
              - dst (Tensor): output bounding boxes with a shape (B, 4, 2).
  
      Note:
          No random number will be generated.
      """
      _common_param_check(batch_size)
      if not isinstance(size, (tuple, list)) and len(size) == 2:
          raise ValueError(
              f"Input size must be a tuple/list of length 2. Got {size}"
          )
      if not (
          isinstance(height, int)
          and height > 0
          and isinstance(width, int)
          and width > 0
      ):
          raise AssertionError(
              f"'height' and 'width' must be integers. Got {height}, {width}."
          )
      if not (height >= size[0] and width >= size[1]):
          raise AssertionError(
              f"Crop size must be smaller than input size. Got ({height}, {width}) and {size}."
          )
  
      # unpack input sizes
      dst_h, dst_w = size
      src_h, src_w = height, width
  
      # compute start/end offsets
      dst_h_half = dst_h / 2
      dst_w_half = dst_w / 2
      src_h_half = src_h / 2
      src_w_half = src_w / 2
  
      start_x = int(src_w_half - dst_w_half)
      start_y = int(src_h_half - dst_h_half)
  
      end_x = start_x + dst_w - 1
      end_y = start_y + dst_h - 1
  
      # [y, x] origin
      # top-left, top-right, bottom-right, bottom-left
      points_src: Tensor = tensor(
          [
              [
                  [start_x, start_y],
                  [end_x, start_y],
                  [end_x, end_y],
                  [start_x, end_y],
              ]
          ],
          device=device,
          dtype=torch.long,
      ).expand(batch_size, -1, -1)
  
      # [y, x] destination
      # top-left, top-right, bottom-right, bottom-left
      points_dst: Tensor = tensor(
          [
              [
                  [0, 0],
                  [dst_w - 1, 0],
                  [dst_w - 1, dst_h - 1],
                  [0, dst_h - 1],
              ]
          ],
          device=device,
          dtype=torch.long,
      ).expand(batch_size, -1, -1)
  
      _input_size = tensor(
          (height, width), device=device, dtype=torch.long
      ).expand(batch_size, -1)
      _output_size = tensor(
          size, device=device, dtype=torch.long
      ).expand(batch_size, -1)
  
      return {
          "src": points_src,
          "dst": points_dst,
          "input_size": _input_size,
          "output_size": _output_size,
      }
  
imports: from typing import Dict, Optional, Tuple, Union
  import torch
  from torch.distributions import Uniform
  from kornia.augmentation.random_generator.base import RandomGeneratorBase
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check, _joint_range_check
  from kornia.core import Device, Tensor, tensor, where, zeros
  from kornia.geometry.bbox import bbox_generator
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 1 for calling a global function with example usage: center_crop_generator(batch_size, height, width, size, device) and returns: dict