class_docstr: Coupling Block following the NICE design.
code: def __init__(
      self, dims_in, dims_c=[], non_linearity="relu", reduction_factor=2
  ):
      super().__init__()
  
      channels = dims_in[0][0]
      self.split_len1 = channels // 2
      self.split_len2 = channels - channels // 2
  
      assert all(
          [dims_c[i][1:] == dims_in[0][1:] for i in range(len(dims_c))]
      ), "Dimensions of input and one or more conditions don't agree."
      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.F = subnet_constructor(
          self.split_len2 + condition_length, self.split_len1
      )
      self.G = subnet_constructor(
          self.split_len1 + condition_length, self.split_len2
      )
  
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 NICECouplingBlock object from inherited base classes: nn with example: obj = NICECouplingBlock(dims_in, dims_c, non_linearity, reduction_factor)