code: def crop_resize(
      frames: tf.Tensor,
      output_h: int,
      output_w: int,
      num_frames: int,
      num_channels: int,
      area_range=(0.3, 1),
      unused_state=None,
      aspect_ratio=(0.5, 2.0),
  ) -> tf.Tensor:
      """First crop clip with jittering and then resizes to (output_h, output_w).
  
      Args:
        frames: A Tensor of dimension [timesteps, input_h, input_w, channels].
        output_h: Size of the height of output.
        output_w: Size of the width of output.
        num_frames: Number of input frames per clip.
        num_channels: Number of channels of the clip.
        area_range: Random crop will preserve this proportion of the area of the
        original frame.
        unused_state: Argument included to be compatible with DeepMind Video Reader
        preprocessing pipeline functions which pass in a state variable.
        aspect_ratio: Aspect ratio range of area based random resizing.
  
      Returns:
        A Tensor of shape [timesteps, output_h, output_w, channels] of type
          frames.dtype.
      """
  
      shape = tf.shape(frames)
      seq_len, channels = int(shape[0]), int(shape[3])
      bbox = tf.constant(
          [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]
      )
      factor = output_w / output_h
      aspect_ratio = (
          aspect_ratio[0] * factor,
          aspect_ratio[1] * factor,
      )
  
      sample_distorted_bbox = tf.image.sample_distorted_bounding_box(
          shape[1:],
          bounding_boxes=bbox,
          min_object_covered=0.1,
          aspect_ratio_range=aspect_ratio,
          area_range=area_range,
          max_attempts=100,
          use_image_if_no_bounding_boxes=True,
      )
      bbox_begin, bbox_size, _ = sample_distorted_bbox
      offset_y, offset_x, _ = tf.unstack(bbox_begin)
      target_height, target_width, _ = tf.unstack(bbox_size)
      size = tf.convert_to_tensor(
          (seq_len, target_height, target_width, channels)
      )
      offset = tf.convert_to_tensor((0, offset_y, offset_x, 0))
  
      frames = tf.slice(frames, offset, size)
      frames = tf.cast(
          tf.image.resize(
              frames,
              (output_h, output_w),
              method=tf.image.ResizeMethod.BICUBIC,
          ),
          frames.dtype,
      )
      frames.set_shape((num_frames, output_h, output_w, num_channels))
      return frames
  
imports: import functools
  import math
  from typing import Optional
  from absl import logging
  from dmvr import builders
  from dmvr import processors
  import simclr.tf2.data_util
  import tensorflow
  from official.vision.image_classification import augment
label_desc: Use image node_id 4 for calling a global function with example usage: crop_resize(frames, output_h, output_w, num_frames, num_channels, area_range, unused_state, aspect_ratio) and returns: frames