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 __init__(
      self,
      dims_in,
      dims_c=[],
      non_linearity="relu",
      reduction_factor=2,
      clamp=5.0,
  ):
      super().__init__()
  
      channels = dims_in[0][0]
      self.ndims = len(dims_in[0])
      self.split_len1 = channels // 2
      self.split_len2 = channels - channels // 2
  
      self.clamp = clamp
      self.max_s = math.exp(clamp)
      self.min_s = math.exp(-clamp)
  
      assert all(
          [
              tuple(dims_c[i][1:]) == tuple(dims_in[0][1:])
              for i in range(len(dims_c))
          ]
      ), f"Dimensions of input and one or more conditions don't agree: {dims_c} vs {dims_in}."
      self.conditional = len(dims_c) > 0
      condition_length = sum([dims_c[i][0] for i in range(len(dims_c))])
  
      subnet_constructor = get_subnet_constructor(
          non_linearity, reduction_factor
      )
      self.s1 = subnet_constructor(
          self.split_len1 + condition_length, self.split_len2 * 2
      )
      self.s2 = subnet_constructor(
          self.split_len2 + condition_length, self.split_len1 * 2
      )
  
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 1 to create a new GLOWCouplingBlock object from inherited base classes: nn with example: obj = GLOWCouplingBlock(dims_in, dims_c, non_linearity, reduction_factor, clamp)