class_docstr: Implements an 'n-tower' MLPDecoder
code: def forward(self, *x: List[torch.Tensor]) -> torch.Tensor:
      # as per the associated model's arrange_model_inputs()
      # first half of the list is the token inputs, the second half is the dense features
      halfway = len(x) // 2
      outputs = []
  
      for i in range(halfway):
          if self.export_type == i or self.export_type == -1:
              tensor = (
                  torch.cat((x[i], x[halfway + i]), 1).half()
                  if precision.FP16_ENABLED
                  else torch.cat((x[i], x[halfway + i]), 1).float()
              )
              # len(tensor i) == i's encoder.embedding_dim + i's dense_dim
              output = getattr(self, f"tower_mlp_{i}")(tensor)
              outputs.append(output)
              if self.export_type == i:
                  return output
  
      return self.mlp(torch.cat(outputs, 1))
  
file_path: pytext/pytext/models/decoders/mlp_decoder_n_tower.py
imports: from typing import List
  import torch
  import torch.nn
  from pytext.config.module_config import Activation
  from pytext.models.decoders.decoder_base import DecoderBase
  from pytext.optimizer import get_activation
  from pytext.utils import precision
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 3 for calling the MLPDecoderNTower obj's underlying member method code with example usage: obj.forward() and returns: self, output