code: def forward(self, z_dist: Tensor, jacobians: Tensor) -> Tensor:
      """Loss function of CS-Flow.
  
      Args:
          z_distribution (Tensor): Latent space image mappings from NF.
          jacobians (Tensor): Jacobians of the distribution
  
      Returns:
          Loss value
      """
      z_dist = torch.cat(
          [
              z_dist[i].reshape(z_dist[i].shape[0], -1)
              for i in range(len(z_dist))
          ],
          dim=1,
      )
      return (
          torch.mean(0.5 * torch.sum(z_dist**2, dim=(1,)) - jacobians)
          / z_dist.shape[1]
      )
  
imports: import torch
  from torch import Tensor, nn
label_desc: Use image node_id 1 for calling the CsFlowLoss obj's underlying member method code with example usage: obj.forward(z_dist, jacobians) and returns: unknown