class_docstr: Implements the transformations for FastSiam.
  
  Input to this transform:
      PIL Image or Tensor.
  
  Output of this transform:
      List of Tensor of length 4.
  
  Applies the following augmentations by default:
      - Random resized crop
      - Random horizontal flip
      - Color jitter
      - Random gray scale
      - Gaussian blur
      - ImageNet normalization
  
  Attributes:
      num_views:
          Number of views (num_views = K+1 where K is the number of target views).
      input_size:
          Size of the input image in pixels.
      cj_prob:
          Probability that color jitter is applied.
      cj_strength:
          Strength of the color jitter. `cj_bright`, `cj_contrast`, `cj_sat`, and
          `cj_hue` are multiplied by this value. For datasets with small images,
          such as CIFAR, it is recommended to set `cj_strength` to 0.5.
      cj_bright:
          How much to jitter brightness.
      cj_contrast:
          How much to jitter constrast.
      cj_sat:
          How much to jitter saturation.
      cj_hue:
          How much to jitter hue.
      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.
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code: def __init__(
      self,
      num_views: int = 4,
      input_size: int = 224,
      cj_prob: float = 0.8,
      cj_strength: float = 1.0,
      cj_bright: float = 0.4,
      cj_contrast: float = 0.4,
      cj_sat: float = 0.4,
      cj_hue: float = 0.1,
      min_scale: float = 0.2,
      random_gray_scale: float = 0.2,
      gaussian_blur: float = 0.5,
      kernel_size: Optional[float] = None,
      sigmas: Tuple[float, float] = (0.1, 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: Union[
          None, Dict[str, List[float]]
      ] = IMAGENET_NORMALIZE,
  ):
      transforms = [
          SimSiamViewTransform(
              input_size=input_size,
              cj_prob=cj_prob,
              cj_strength=cj_strength,
              cj_bright=cj_bright,
              cj_contrast=cj_contrast,
              cj_sat=cj_sat,
              cj_hue=cj_hue,
              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,
          )
          for _ in range(num_views)
      ]
      super().__init__(transforms=transforms)
  
imports: from typing import Dict, List, Optional, Tuple, Union
  from lightly.transforms.multi_view_transform import MultiViewTransform
  from lightly.transforms.simsiam_transform import SimSiamViewTransform
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 1 to create a new FastSiamTransform object from inherited base classes: MultiViewTransform with example: obj = FastSiamTransform(num_views, input_size, cj_prob, cj_strength, cj_bright, cj_contrast, cj_sat, cj_hue, min_scale, random_gray_scale, gaussian_blur, kernel_size, sigmas, vf_prob, hf_prob, rr_prob, rr_degrees, normalize)