class_docstr: ``nn.Module`` wrapper for ``pytorchvideo.transforms.functional.random_resized_crop``.
class_docstr_tok: ["``","nn.Module","``","wrapper","for","``","pytorchvideo.transforms.functional.random_resized_crop","``","."]
docstr: Args:
      x (torch.Tensor): Input video tensor with shape (C, T, H, W).
docstr_tok: ["Args",":","x","(","torch.Tensor",")",":","Input","video","tensor","with","shape","(","C",",","T",",","H",",","W",")","."]
code: def __call__(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x (torch.Tensor): Input video tensor with shape (C, T, H, W).
      """
      return pytorchvideo.transforms.functional.random_resized_crop(
          x,
          self._target_height,
          self._target_width,
          self._scale,
          self._aspect_ratio,
          self._shift,
          self._log_uniform_ratio,
          self._interpolation,
          self._num_tries,
      )
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the RandomResizedCrop obj's underlying member method code with example usage: obj.__call__(x) and returns: pytorchvideo