code: def forward(
      self, logits: List[torch.Tensor]
  ) -> List[List[Dict[str, float]]]:
      results: List[List[Dict[str, float]]] = []
      for idx, sc in enumerate(self.scores):
          logit = logits[idx]
          # flatten from [batch_size, ..., label_set_size] to
          # [batch_size, label_set_size]
          # must flatten because jit doesn't support dynamic return type
          flattened_logit = logit.view(-1, logit.size()[-1])
          results.append(sc(flattened_logit))
  
      return results
  
imports: import functools
  import operator
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn
  from caffe2.python import core
  from pytext.data.tensorizers import Tensorizer
  from pytext.utils.usage import log_class_usage
  from torch import jit
  from .doc_classification_output_layer import ClassificationOutputLayer
  from .output_layer_base import OutputLayerBase
label_desc: Use image node_id 2 for calling the MultiLabelClassificationScores obj's underlying member method code with example usage: obj.forward(logits) and returns: results