docstr: Note:
      Set `reverse` to True for inference.
  
  Shapes:
      - x: :math:`[B, C, T]`
      - x_mask: :math:`[B, 1, T]`
      - g: :math:`[B, C, 1]`
docstr_tok: ["Note",":","Set","`","reverse","`","to","True","for","inference",".","Shapes",":","-","x",":",":","math",":","`","[","B",",","C",",","T","]","`","-","x_mask",":",":","math",":","`","[","B",",","1",",","T","]","`","-","g",":",":","math",":","`","[","B",",","C",",","1","]","`"]
code: def forward(self, x, x_mask, g=None, reverse=False):
      """
      Note:
          Set `reverse` to True for inference.
  
      Shapes:
          - x: :math:`[B, C, T]`
          - x_mask: :math:`[B, 1, T]`
          - g: :math:`[B, C, 1]`
      """
      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, log_scale = torch.split(stats, [self.half_channels] * 2, 1)
      else:
          m = stats
          log_scale = torch.zeros_like(m)
  
      if not reverse:
          x1 = m + x1 * torch.exp(log_scale) * x_mask
          x = torch.cat([x0, x1], 1)
          logdet = torch.sum(log_scale, [1, 2])
          return x, logdet
      else:
          x1 = (x1 - m) * torch.exp(-log_scale) * x_mask
          x = torch.cat([x0, x1], 1)
          return x
  
imports: import math
  import torch
  from torch import nn
  from TTS.tts.layers.glow_tts.glow import WN
  from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
  from TTS.tts.utils.helpers import sequence_mask
label_desc: Use image node_id 2 for calling the ResidualCouplingBlock obj's underlying member method code with example usage: obj.forward(x, x_mask, g, reverse) and returns: x,  logdet, x