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 jacobian(self, x, c=[], rev=False):
      return self.last_jac
  
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 5 for calling the GLOWCouplingBlock obj's underlying member method code with example usage: obj.jacobian(x, c, rev) and returns: self