class_docstr: A custom sequence of transforms that randomly performs Color jitter,
  Gaussian Blur and Grayscaling on the given clip.
  
  Particularly useful for the SSL tasks like SimCLR, MoCoV2, BYOL, etc.
  
  Args:
      bri_con_sat (list[float]): A list of 3 floats reprsenting brightness,
      constrast and staturation coefficients to use for the
      `torchvision.transforms.ColorJitter` transform.
      hue (float): Heu value to use in the `torchvision.transforms.ColorJitter`
      transform.
      p_color_jitter (float): The probability with which the Color jitter transform
      is randomly applied on the given clip.
      p_convert_gray (float): The probability with which the given clip is randomly
      coverted into grayscale.
      p_gaussian_blur (float): The probability with which the Gaussian transform
      is randomly applied on the given clip.
      gaussian_blur_sigma (list[float]): A list of 2 floats with in which
      the blur radius is randomly sampled for Gaussian blur transform.
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docstr: Args:
      frames (tensor): frames of images sampled from the video. The
      dimension is `channel` x `num frames` x `height` x `width`.
  Returns:
      frames (tensor): frames of images sampled from the video. The
      dimension is `channel` x `num frames` x `height` x `width`.
docstr_tok: ["Args",":","frames","(","tensor",")",":","frames","of","images","sampled","from","the","video",".","The","dimension","is","`","channel","`","x","`","num","frames","`","x","`","height","`","x","`","width","`",".","Returns",":","frames","(","tensor",")",":","frames","of","images","sampled","from","the","video",".","The","dimension","is","`","channel","`","x","`","num","frames","`","x","`","height","`","x","`","width","`","."]
code: def __call__(self, frames: torch.Tensor) -> torch.Tensor:
      """
      Args:
          frames (tensor): frames of images sampled from the video. The
          dimension is `channel` x `num frames` x `height` x `width`.
      Returns:
          frames (tensor): frames of images sampled from the video. The
          dimension is `channel` x `num frames` x `height` x `width`.
      """
      c, t, h, w = frames.shape
      frames = frames.view(c, t * h, w)
      frames = self.color_jitter(frames)  # pyre-ignore[6,9]
      frames = frames.view(c, t, h, w)
  
      return frames
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 2 for calling the ColorJitterVideoSSl obj's underlying member method code with example usage: obj.__call__(frames) and returns: frames