code: def covariance_loss(x: Tensor) -> Tensor:
      """Returns VICReg covariance loss.
  
      Generalized version of the covariance loss with support for tensors with more than
      two dimensions. Adapted from VICRegL:
      https://github.com/facebookresearch/VICRegL/blob/803ae4c8cd1649a820f03afb4793763e95317620/main_vicregl.py#L299
  
      Args:
          x:
              Tensor with shape (batch_size, ..., dim).
      """
      x = x - x.mean(dim=0)
      batch_size = x.size(0)
      dim = x.size(-1)
      # nondiag_mask has shape (dim, dim) with 1s on all non-diagonal entries.
      nondiag_mask = ~torch.eye(dim, device=x.device, dtype=torch.bool)
      # cov has shape (..., dim, dim)
      cov = torch.einsum("b...c,b...d->...cd", x, x) / (batch_size - 1)
      loss = cov[..., nondiag_mask].pow(2).sum(-1) / dim
      return loss.mean()
  
file_path: lightly/lightly/loss/vicreg_loss.py
imports: import torch
  import torch.distributed
  import torch.nn.functional
  from torch import Tensor
  from lightly.utils.dist import gather
label_desc: Use image node_id 3 for calling a global function with example usage: covariance_loss(x) and returns: loss