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","``","."]
code: def __init__(
      self,
      target_height: int,
      target_width: int,
      scale: Tuple[float, float],
      aspect_ratio: Tuple[float, float],
      shift: bool = False,
      log_uniform_ratio: bool = True,
      interpolation: str = "bilinear",
      num_tries: int = 10,
  ) -> None:
      super().__init__()
      self._target_height = target_height
      self._target_width = target_width
      self._scale = scale
      self._aspect_ratio = aspect_ratio
      self._shift = shift
      self._log_uniform_ratio = log_uniform_ratio
      self._interpolation = interpolation
      self._num_tries = 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 1 to create a new RandomResizedCrop object from inherited base classes: torch.nn with example: obj = RandomResizedCrop(target_height, target_width, scale, aspect_ratio, shift, log_uniform_ratio, interpolation, num_tries)