class_docstr: Coupling Block following the GLOW design. The only difference to the RealNVP coupling blocks, is the fact that it
  uses a single subnetwork to jointly predict [s_i, t_i], instead of two separate subnetworks. This reduces
  computational cost and speeds up learning. clamp: Soft clamping for the multiplicative component. The amplification
  or attenuation of each input dimension can be at most ±exp(clamp).
code: def forward(self, x, c=[], rev=False):
      x1, x2 = (x[:, :, : self.split_len1], x[:, :, self.split_len1 :])
  
      if not rev:
          s2, t2 = x1.clone(), x2.clone()
          y1 = self.e(s2) * x1 + t2
  
          r1 = self.s1(
              torch.cat([y1, *c], 1) if self.conditional else y1
          )
          s1, t1 = r1[:, : self.split_len2], r1[:, self.split_len2 :]
          y2 = self.e(s1) * x2 + t1
          self.last_jac = torch.sum(
              self.log_e(s1), dim=tuple(range(1, self.ndims + 1))
          ) + torch.sum(
              self.log_e(s2), dim=tuple(range(1, self.ndims + 1))
          )
  
      else:  # names of x and y are swapped!
          r1 = self.s1(
              torch.cat([x1, *c], 1) if self.conditional else x1
          )
          s1, t1 = r1[:, : self.split_len2], r1[:, self.split_len2 :]
          y2 = (x2 - t1) / self.e(s1)
  
          r2 = self.s2(
              torch.cat([y2, *c], 1) if self.conditional else y2
          )
          s2, t2 = r2[:, : self.split_len1], r2[:, self.split_len1 :]
          y1 = (x1 - t2) / self.e(s2)
          self.last_jac = -torch.sum(
              self.log_e(s1), dim=tuple(range(1, self.ndims + 1))
          ) - torch.sum(
              self.log_e(s2), dim=tuple(range(1, self.ndims + 1))
          )
  
      return [torch.cat((y1, y2), 1)]
  
imports: import math
  import torch
  from torch import nn
  from transformers.activations import get_activation
  from ..configuration import AdapterFusionConfig, BnConfig
  from ..context import ForwardContext
label_desc: Use image node_id 4 for calling the GLOWCouplingBlock obj's underlying member method code with example usage: obj.forward(x, c, rev) and returns: list