class_docstr: Extract center crop at the same location for a list of images
  
  Args:
  size (sequence or int): Desired output size for the
  crop in format (h, w)
class_docstr_tok: ["Extract","center","crop","at","the","same","location","for","a","list","of","images","Args",":","size","(","sequence","or","int",")",":","Desired","output","size","for","the","crop","in","format","(","h",",","w",")"]
docstr: Args:
  img (PIL.Image or numpy.ndarray): List of images to be cropped
  in format (h, w, c) in numpy.ndarray
  
  Returns:
  PIL.Image or numpy.ndarray: Cropped list of images
docstr_tok: ["Args",":","img","(","PIL.Image","or","numpy.ndarray",")",":","List","of","images","to","be","cropped","in","format","(","h",",","w",",","c",")","in","numpy.ndarray","Returns",":","PIL.Image","or","numpy.ndarray",":","Cropped","list","of","images"]
code: def __call__(self, clip):
      """
      Args:
      img (PIL.Image or numpy.ndarray): List of images to be cropped
      in format (h, w, c) in numpy.ndarray
  
      Returns:
      PIL.Image or numpy.ndarray: Cropped list of images
      """
      h, w = self.size
      if isinstance(clip[0], np.ndarray):
          im_h, im_w, im_c = clip[0].shape
      elif isinstance(clip[0], PIL.Image.Image):
          im_w, im_h = clip[0].size
      else:
          raise TypeError(
              "Expected numpy.ndarray or PIL.Image"
              + "but got list of {0}".format(type(clip[0]))
          )
      if w > im_w or h > im_h:
          error_msg = (
              "Initial image size should be larger then "
              "cropped size but got cropped sizes : ({w}, {h}) while "
              "initial image is ({im_w}, {im_h})".format(
                  im_w=im_w, im_h=im_h, w=w, h=h
              )
          )
          raise ValueError(error_msg)
  
      x1 = int(round((im_w - w) / 2.0))
      y1 = int(round((im_h - h) / 2.0))
      cropped = F.crop_clip(clip, y1, x1, h, w)
  
      return cropped
  
imports: import numbers
  import random
  import numpy
  import PIL
  import torch
  import torchvision
  from .None import functional
label_desc: Use image node_id 2 for calling the CenterCrop obj's underlying member method code with example usage: obj.__call__(clip) and returns: cropped