code: def forward(self, txt_enc, spk_emb, x, lens):
      if x is not None:
          x = self.normalize(x)
  
      txt_enc = self.bottleneck_layer(txt_enc, lens)
      spk_emb_expanded = spk_emb[..., None].expand(
          -1, -1, txt_enc.shape[2]
      )
      context = torch.cat((txt_enc, spk_emb_expanded), 1)
      x_hat = self.feat_pred_fn(context, lens)
      outputs = {"x_hat": x_hat, "x": x}
      return outputs
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nemo.collections.tts.modules.common import ConvLSTMLinear
  from nemo.collections.tts.modules.submodules import ConvNorm, MaskedInstanceNorm1d
  from nemo.collections.tts.modules.transformer import FFTransformer
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
label_desc: Use image node_id 2 for calling the DAP obj's underlying member method code with example usage: obj.forward(txt_enc, spk_emb, x, lens) and returns: outputs