class_docstr: auxiliary pseudo label loss for UASR.
code: def __init__(
      self,
      weight: float = 1.0,
      input_dim: int = 128,
      output_dim: int = 64,
      downsample_rate: int = 2,
      ignore_index: int = -1,
      reduction: str = "none",
  ):
      super().__init__()
      assert check_argument_types()
  
      self.weight = weight
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.downsample_rate = downsample_rate
      self.ignore_index = ignore_index
      self.reduction = reduction
  
      if self.weight > 0:
          self.decoder = torch.nn.Linear(
              self.input_dim, self.output_dim
          )
  
file_path: espnet/espnet2/uasr/loss/pseudo_label_loss.py
imports: import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.uasr.loss.abs_loss import AbsUASRLoss
  from espnet2.utils.types import str2bool
label_desc: Use image node_id 1 to create a new UASRPseudoLabelLoss object from inherited base classes: AbsUASRLoss with example: obj = UASRPseudoLabelLoss(weight, input_dim, output_dim, downsample_rate, ignore_index, reduction)