code: def forward(self, x, x_mask, g=None, reverse=False):
      x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
      h = self.pre(x0) * x_mask
      h = self.enc(h, x_mask, g=g)
      stats = self.post(h) * x_mask
      if not self.mean_only:
          m, logs = torch.split(stats, [self.half_channels] * 2, 1)
      else:
          m = stats
          logs = torch.zeros_like(m)
  
      if not reverse:
          x1 = m + x1 * torch.exp(logs) * x_mask
          x = torch.cat([x0, x1], 1)
          logdet = torch.sum(logs, [1, 2])
          return x, logdet
      else:
          x1 = (x1 - m) * torch.exp(-logs) * x_mask
          x = torch.cat([x0, x1], 1)
          return x
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  import TTS.vc.modules.freevc.commons
  from TTS.vc.modules.freevc.commons import get_padding, init_weights
label_desc: Use image node_id 2 for calling the ResidualCouplingLayer obj's underlying member method code with example usage: obj.forward(x, x_mask, g, reverse) and returns: x,  logdet, x