code: def g3_loss(
      d_outputs,
      predictions,
      targets,
      length,
      l1LossCoeff,
      klLossCoeff,
      z_mean=None,
      z_logvar=None,
      reduction="mean",
  ):
      """Calculates the loss of the generator given the discriminator outputs"""
      discrimloss = 0.5 * ((d_outputs - 1) ** 2)
      l1norm = torch.nn.functional.l1_loss(
          predictions, targets, reduction="none"
      )
  
      if not (
          z_mean is None
      ):  # This will determine if model is being trained as a vae
          ZERO = torch.zeros_like(z_mean)
          distq = torch.distributions.normal.Normal(
              z_mean, torch.exp(z_logvar) ** (1 / 2)
          )
          distp = torch.distributions.normal.Normal(
              ZERO, torch.exp(ZERO) ** (1 / 2)
          )
          kl = torch.distributions.kl.kl_divergence(distq, distp)
          kl = kl.sum(axis=1).sum(axis=1).mean()
      else:
          kl = 0
      if reduction == "mean":
          return (
              discrimloss.mean()
              + l1LossCoeff * l1norm.mean()
              + klLossCoeff * kl
          )
      elif reduction == "batch":
          dloss = discrimloss.view(discrimloss.size(0), -1).mean(1)
          lloss = l1norm.view(l1norm.size(0), -1).mean(1)
          return dloss + l1LossCoeff * lloss + klLossCoeff * kl
  
file_path: speechbrain/speechbrain/lobes/models/segan_model.py
imports: import torch
  import torch.nn
  import torch.utils.data
  import torch.nn.functional
  from math import floor
label_desc: Use image node_id 3 for calling a global function with example usage: g3_loss(d_outputs, predictions, targets, length, l1LossCoeff, klLossCoeff, z_mean, z_logvar, reduction) and returns: unknown, unknown