code: def uniform_crop(
      images, size, spatial_idx, boxes=None, scale_size=None
  ):
      """
      Perform uniform spatial sampling on the images and corresponding boxes.
      Args:
          images (tensor): images to perform uniform crop. The dimension is
              `num frames` x `channel` x `height` x `width`.
          size (int): size of height and weight to crop the images.
          spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width
              is larger than height. Or 0, 1, or 2 for top, center, and bottom
              crop if height is larger than width.
          boxes (ndarray or None): optional. Corresponding boxes to images.
              Dimension is `num boxes` x 4.
          scale_size (int): optinal. If not None, resize the images to scale_size before
              performing any crop.
      Returns:
          cropped (tensor): images with dimension of
              `num frames` x `channel` x `size` x `size`.
          cropped_boxes (ndarray or None): the cropped boxes with dimension of
              `num boxes` x 4.
      """
      assert spatial_idx in [0, 1, 2]
      ndim = len(images.shape)
      if ndim == 3:
          images = images.unsqueeze(0)
      height = images.shape[2]
      width = images.shape[3]
  
      if scale_size is not None:
          if width <= height:
              width, height = scale_size, int(
                  height / width * scale_size
              )
          else:
              width, height = (
                  int(width / height * scale_size),
                  scale_size,
              )
          images = torch.nn.functional.interpolate(
              images,
              size=(height, width),
              mode="bilinear",
              align_corners=False,
          )
  
      y_offset = int(math.ceil((height - size) / 2))
      x_offset = int(math.ceil((width - size) / 2))
  
      if height > width:
          if spatial_idx == 0:
              y_offset = 0
          elif spatial_idx == 2:
              y_offset = height - size
      else:
          if spatial_idx == 0:
              x_offset = 0
          elif spatial_idx == 2:
              x_offset = width - size
      cropped = images[
          :, :, y_offset : y_offset + size, x_offset : x_offset + size
      ]
      cropped_boxes = (
          crop_boxes(boxes, x_offset, y_offset)
          if boxes is not None
          else None
      )
      if ndim == 3:
          cropped = cropped.squeeze(0)
      return cropped, cropped_boxes
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 6 for calling a global function with example usage: uniform_crop(images, size, spatial_idx, boxes, scale_size) and returns: cropped,  cropped_boxes