class_docstr: Implementation of the loss function from MMCR [0] using Manifold Capacity.
  All hyperparameters are set to the default values from the paper for ImageNet.
  
  - [0]: Efficient Coding of Natural Images using Maximum Manifold Capacity
          Representations, 2023, https://arxiv.org/pdf/2303.03307.pdf
  
   Examples:
  
  
      >>> # initialize loss function
      >>> loss_fn = MMCRLoss()
      >>> transform = MMCRTransform(k=2)
      >>>
      >>> # transform images, then feed through encoder and projector
      >>> x = transform(x)
      >>> online = online_network(x)
      >>> momentum = momentum_network(x)
      >>>
      >>> # calculate loss
      >>> loss = loss_fn(online, momentum)
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code: def __init__(self, lmda: float = 5e-3):
      super().__init__()
      if lmda < 0:
          raise ValueError("lmda must be greater than or equal to 0")
  
      self.lmda = lmda
  
imports: import torch
  import torch.nn
  from torch.linalg import svd
label_desc: Use image node_id 1 to create a new MMCRLoss object from inherited base classes: nn with example: obj = MMCRLoss(lmda)