code: def hubert_loss(
      logit_m: Optional[Tensor],
      logit_u: Optional[Tensor],
      feature_penalty: Tensor,
      label: Optional[Tensor] = None,
      masked_weight: float = 1.0,
      unmasked_weight: float = 0.0,
      feature_weight: float = 10.0,
      reduction: str = "sum",
  ) -> Tuple[Tensor, float]:
      """Compute the cross-entropy loss on HuBERT masked and non-masked logits.
  
      Args:
          logit_m (Tensor or None): The masked logit Tensor of dimension `(masked_frames, final_dim)`.
          logit_u (Tensor or None): The non-masked logit Tensor of dimension `(unmasked_frames, final_dim)`.
          feature_penalty (Tensor): The feature mean value for additional penalty loss.
          masked_weight (float, optional): The weight for masked cross-entropy loss (Default: ``1.0``).
          unmasked_weight (float, optional): The weight for non-masked cross-entropy loss (Default: ``0.0``).
          feature_weight (float, optional): The weight for feature penalty loss (Default: ``10.0``).
          reduction (str, optional): The reduction method for cross-entropy loss (Default: ``"sum"``).
  
      Returns:
          (Tensor, float)
          Tensor: The desired loss Tensor.
          float: Number of frames used in loss computation.
      """
      num_frame = 0.0
      loss = 0.0
      if logit_m is not None:
          target_m = torch.zeros(
              logit_m.shape[0], dtype=torch.long, device=logit_m.device
          )
          loss_m = F.cross_entropy(
              logit_m, target_m, reduction=reduction
          )
          loss += loss_m * masked_weight
          num_frame += logit_m.shape[0]
      if logit_u is not None:
          target_u = torch.zeros(
              logit_u.shape[0], dtype=torch.long, device=logit_m.device
          )
          loss_u = F.cross_entropy(
              logit_u, target_u, reduction=reduction
          )
          loss += loss_u * unmasked_weight
          num_frame += logit_u.shape[0]
      loss += feature_penalty * feature_weight * num_frame
      return loss, num_frame
  
imports: from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: hubert_loss(logit_m, logit_u, feature_penalty, label, masked_weight, unmasked_weight, feature_weight, reduction) and returns: loss,  num_frame