class_docstr: Implements the transformations for MoCo v1.
  
  For MoCo v2, simply use the SimCLR settings.
  
  Attributes:
      input_size:
          Size of the input image in pixels.
      cj_prob:
          Probability that color jitter is applied.
      cj_strength:
          Strength of the color jitter.
      min_scale:
          Minimum size of the randomized crop relative to the input_size.
      random_gray_scale:
          Probability of conversion to grayscale.
      gaussian_blur:
          Probability of Gaussian blur.
      kernel_size:
          Will be deprecated in favor of `sigmas` argument. If set, the old behavior applies and `sigmas` is ignored.
          Used to calculate sigma of gaussian blur with kernel_size * input_size.
      sigmas:
          Tuple of min and max value from which the std of the gaussian kernel is sampled.
          Is ignored if `kernel_size` is set.
      vf_prob:
          Probability that vertical flip is applied.
      hf_prob:
          Probability that horizontal flip is applied.
      rr_prob:
          Probability that random rotation is applied.
      rr_degrees:
          Range of degrees to select from for random rotation. If rr_degrees is None,
          images are rotated by 90 degrees. If rr_degrees is a (min, max) tuple,
          images are rotated by a random angle in [min, max]. If rr_degrees is a
          single number, images are rotated by a random angle in
          [-rr_degrees, +rr_degrees]. All rotations are counter-clockwise.
      normalize:
          Dictionary with 'mean' and 'std' for torchvision.transforms.Normalize.
  
  Examples:
  
      >>> # MoCo v1 for ImageNet
      >>> collate_fn = MoCoCollateFunction()
      >>>
      >>> # MoCo v1 for CIFAR-10
      >>> collate_fn = MoCoCollateFunction(
      >>>     input_size=32,
      >>> )
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code: def __init__(
      self,
      input_size: int = 224,
      cj_prob: float = 0.8,
      cj_strength: float = 0.4,
      min_scale: float = 0.2,
      random_gray_scale: float = 0.2,
      gaussian_blur: float = 0.0,
      kernel_size: Optional[float] = None,
      sigmas: Tuple[float, float] = (0.2, 2),
      vf_prob: float = 0.0,
      hf_prob: float = 0.5,
      rr_prob: float = 0.0,
      rr_degrees: Union[None, float, Tuple[float, float]] = None,
      normalize: dict = imagenet_normalize,
  ):
      super(MoCoCollateFunction, self).__init__(
          input_size=input_size,
          cj_prob=cj_prob,
          cj_bright=cj_strength,
          cj_contrast=cj_strength,
          cj_sat=cj_strength,
          cj_hue=cj_strength,
          min_scale=min_scale,
          random_gray_scale=random_gray_scale,
          gaussian_blur=gaussian_blur,
          kernel_size=kernel_size,
          sigmas=sigmas,
          vf_prob=vf_prob,
          hf_prob=hf_prob,
          rr_prob=rr_prob,
          rr_degrees=rr_degrees,
          normalize=normalize,
      )
  
imports: import math
  from multiprocessing import Value
  from typing import List, Optional, Tuple, Union
  from warnings import warn
  import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
  from PIL import Image
  from lightly.transforms import GaussianBlur, Jigsaw, RandomSolarization
  from lightly.transforms.random_crop_and_flip_with_grid import RandomResizedCropAndFlip
  from lightly.transforms.rotation import random_rotation_transform
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 1 to create a new MoCoCollateFunction object from inherited base classes: ImageCollateFunction with example: obj = MoCoCollateFunction(input_size, cj_prob, cj_strength, min_scale, random_gray_scale, gaussian_blur, kernel_size, sigmas, vf_prob, hf_prob, rr_prob, rr_degrees, normalize)