code: def forward(self, x, labels=None):
      h = self.enc(x)
      logits = self.head(h)
      if labels is None:
          return logits
      else:
          if self.distribute_zero_label:
              oh_labels = nn.functional.one_hot(
                  labels, num_classes=self.num_classes
              )
              zeros_indices = (labels == 0).unsqueeze(-1)
              # Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.
              zero_extra_mass = torch.full_like(
                  oh_labels,
                  dtype=torch.float,
                  fill_value=0.2 / (self.num_classes - 1),
              )
              zero_extra_mass[:, 0] = -0.2
              zero_extra_mass = zero_extra_mass * zeros_indices
              oh_labels = oh_labels + zero_extra_mass
          else:
              oh_labels = labels
          loss = nn.functional.cross_entropy(logits, oh_labels)
          return loss
  
file_path: TTS/TTS/tts/layers/tortoise/classifier.py
imports: import torch
  import torch.nn
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, Downsample, Upsample, normalization, zero_module
label_desc: Use image node_id 2 for calling the AudioMiniEncoderWithClassifierHead obj's underlying member method code with example usage: obj.forward(x, labels) and returns: logits, loss