code: def forward(self, logits: torch.Tensor):
      # In pure python, this code would be implemented as follows:
      #   scores = self.score_function(logits)
      #   return [
      #     {class: score for class, score in zip(self.classes, example_scores}
      #     for example_scores in scores.tolist()
      #   ]
      # Extra verbosity is due to jit.script.
      if self.score_function_dim is None:
          scores = self.score_function(logits)
      else:
          scores = self.score_function(
              logits, dim=self.score_function_dim
          )
  
      results = jit.annotate(List[Dict[str, float]], [])
      for example_scores in scores.chunk(len(scores)):
          example_scores = example_scores.squeeze(dim=0)
          example_response = jit.annotate(Dict[str, float], {})
          for i in range(len(self.classes)):
              example_response[self.classes[i]] = float(
                  example_scores[i].item()
              )
          results.append(example_response)
      return results
  
imports: from typing import Dict, List, Optional, Union
  import torch
  import torch.nn.functional
  from caffe2.python import core
  from pytext.common import Padding
  from pytext.config.component import create_loss
  from pytext.data.utils import Vocabulary
  from pytext.fields import FieldMeta
  from pytext.loss import AUCPRHingeLoss, BinaryCrossEntropyLoss, BinaryCrossEntropyWithLogitsLoss, CrossEntropyLoss, HingeLoss, KLDivergenceBCELoss, KLDivergenceCELoss, LabelSmoothedCrossEntropyLoss, MultiLabelSoftMarginLoss
  from pytext.utils.label import get_custom_or_automatic_label_weights
  from pytext.utils.typing import WeightingMethod
  from torch import jit
  from .output_layer_base import OutputLayerBase
  from .utils import OutputLayerUtils
label_desc: Use image node_id 2 for calling the ClassificationScores obj's underlying member method code with example usage: obj.forward(logits) and returns: results