class_docstr: Implements the transformations for MMCR[0], which
  are based on BYOL[1].
  
  Input to this transform:
      PIL Image or Tensor.
  
  Output of this transform:
      List of Tensor of length k.
  
  Applies the following augmentations by default:
      - Random resized crop
      - Random horizontal flip
      - Color jitter
      - Random gray scale
      - Gaussian blur
      - Solarization
      - ImageNet normalization
  
  Please refer to the BYOL implementation for additional details.
  
  - [0]: Efficient Coding of Natural Images using Maximum Manifold Capacity
          Representations, 2023, https://arxiv.org/pdf/2303.03307.pdf
  - [1]: Bootstrap Your Own Latent, 2020, https://arxiv.org/pdf/2006.07733.pdf
  
  
  Input to this transform:
      PIL Image or Tensor.
  
  Output of this transform:
      List of tensors of length k.
  
  Attributes:
      k: Number of views.
      transform: The transform to apply to each view.
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code: def __init__(
      self,
      k: int = 8,
      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 = 0.2,
      cj_hue: float = 0.1,
      min_scale: float = 0.08,
      random_gray_scale: float = 0.2,
      gaussian_blur: float = 1.0,
      solarization_prob: float = 0.0,
      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,
  ):
      if k < 1:
          raise ValueError("k must be greater than or equal to 1")
      transform = BYOLView1Transform(
          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,
          solarization_prob=solarization_prob,
          kernel_size=kernel_size,
          sigmas=sigmas,
          vf_prob=vf_prob,
          hf_prob=hf_prob,
          rr_prob=rr_prob,
          rr_degrees=rr_degrees,
          normalize=normalize,
      )
      super().__init__(transforms=[transform] * k)
  
imports: from typing import Dict, List, Optional, Tuple, Union
  from lightly.transforms.byol_transform import BYOLView1Transform
  from lightly.transforms.multi_view_transform import MultiViewTransform
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 1 to create a new MMCRTransform object from inherited base classes: MultiViewTransform with example: obj = MMCRTransform(k, input_size, cj_prob, cj_strength, cj_bright, cj_contrast, cj_sat, cj_hue, min_scale, random_gray_scale, gaussian_blur, solarization_prob, kernel_size, sigmas, vf_prob, hf_prob, rr_prob, rr_degrees, normalize)