class_docstr: Use MLP to fuse different models' features (single-modal and multimodal).
  Specifically, it adapts the features of each model to specified dimensions,
  concatenates the adapted features, and fuses the features through MLP.
code: def get_output_dict(
      self,
      features: torch.Tensor,
      logits: torch.Tensor,
      multimodal_logits: List[torch.Tensor],
  ):
      fusion_output = {
          self.prefix: {
              LOGITS: logits,
              FEATURES: features,
          }
      }
      if self.loss_weight is not None:
          output = {}
          for per_model, per_logits in zip(
              self.model, multimodal_logits
          ):
              per_output = {per_model.prefix: {}}
              per_output[per_model.prefix][WEIGHT] = torch.tensor(
                  self.loss_weight
              ).to(per_logits.dtype)
              per_output[per_model.prefix][LOGITS] = per_logits
              output.update(per_output)
          fusion_output[self.prefix].update(
              {WEIGHT: torch.tensor(1.0).to(logits)}
          )
          output.update(fusion_output)
          return output
      else:
          return fusion_output
  
file_path: autogluon/multimodal/src/autogluon/multimodal/models/fusion/fusion_mlp.py
imports: import logging
  from typing import List, Optional
  import torch
  from torch import nn
  from ...constants import AUTOMM, FEATURES, LABEL, LOGITS, WEIGHT
  from ..mlp import MLP
  from ..utils import init_weights, run_model
  from .base import AbstractMultimodalFusionModel
label_desc: Use image node_id 5 for calling the MultimodalFusionMLP obj's underlying member method code with example usage: obj.get_output_dict(features, logits, multimodal_logits) and returns: output, fusion_output