class_docstr: Dilated depth separable convolutional based spline flow.
  
  Args:
      in_channels (int): Number of input tensor channels.
      hidden_channels (int): Number of in network channels.
      kernel_size (int): Convolutional kernel size.
      num_layers (int): Number of convolutional layers.
      num_bins (int, optional): Number of spline bins. Defaults to 10.
      tail_bound (float, optional): Tail bound for PRQT. Defaults to 5.0.
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)
      h = self.convs(h, x_mask, g=g)
      h = self.proj(h) * x_mask
  
      b, c, t = x0.shape
      h = h.reshape(b, c, -1, t).permute(
          0, 1, 3, 2
      )  # [b, cx?, t] -> [b, c, t, ?]
  
      unnormalized_widths = h[..., : self.num_bins] / math.sqrt(
          self.hidden_channels
      )
      unnormalized_heights = h[
          ..., self.num_bins : 2 * self.num_bins
      ] / math.sqrt(self.hidden_channels)
      unnormalized_derivatives = h[..., 2 * self.num_bins :]
  
      x1, logabsdet = piecewise_rational_quadratic_transform(
          x1,
          unnormalized_widths,
          unnormalized_heights,
          unnormalized_derivatives,
          inverse=reverse,
          tails="linear",
          tail_bound=self.tail_bound,
      )
  
      x = torch.cat([x0, x1], 1) * x_mask
      logdet = torch.sum(logabsdet * x_mask, [1, 2])
      if not reverse:
          return x, logdet
      return x
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.generic.normalization import LayerNorm2
  from TTS.tts.layers.vits.transforms import piecewise_rational_quadratic_transform
label_desc: Use image node_id 2 for calling the ConvFlow obj's underlying member method code with example usage: obj.forward(x, x_mask, g, reverse) and returns: x, x,  logdet