code: def forward(
      self,
      src_data: Tensor,
      tgt_data: Tensor,
      src_feats: Optional[Tensor] = None,
      tgt_feats: Optional[Tensor] = None,
      src_nan_mask: Optional[BoolTensor] = None,
      tgt_nan_mask: Optional[BoolTensor] = None,
      src_length: Optional[LongTensor] = None,
      tgt_length: Optional[LongTensor] = None,
  ) -> Tensor:
      src_loss = self.src(src_data, src_feats, src_nan_mask, src_length)
      tgt_loss = self.tgt(tgt_data, tgt_feats, tgt_nan_mask, tgt_length)
      if not self.forecast_target:
          tgt_loss = tgt_loss - self.tgt.tradeoff * self.tgt.fc_loss
      # after rescaling, balance two losses
      if self.balance_loss:
          src_scale = self.src._normalizer._buffers["scale"]
          tgt_scale = self.tgt._normalizer._buffers["scale"]
          src_scale = src_scale.view(src_scale.size(0), -1)
          tgt_scale = tgt_scale.view(tgt_scale.size(0), -1)
          weight = pt.mean(tgt_scale / src_scale, dim=1)
          src_loss = src_loss * weight
      loss = src_loss + tgt_loss
      return loss
  
file_path: gluonts/src/gluonts/nursery/daf/estimator/domains.py
imports: from typing import Optional, List, Tuple, Union, Iterator
  from collections import defaultdict
  import warnings
  import math
  import torch
  from torch import Tensor, LongTensor, BoolTensor
  from torch import nn
  from torch.nn import Parameter, functional
  from .modules import AttentionEstimator, AdversarialEstimator
  from ..network import AttentionKernel, AttentionBlock, AdversarialBlock
label_desc: Use image node_id 2 for calling the DomAdaptEstimator obj's underlying member method code with example usage: obj.forward(src_data, tgt_data, src_feats, tgt_feats, src_nan_mask, tgt_nan_mask, src_length, tgt_length) and returns: loss