code: def forward(
      self,
      logits: torch.Tensor,
      context: Optional[Dict[str, torch.Tensor]] = None,
  ) -> List[List[Dict[str, float]]]:
      scores: torch.Tensor = F.log_softmax(logits, 2)
      return _get_prediction_from_scores(scores, self.classes)
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  import torch.jit
  import torch.nn
  import torch.nn.functional
  from caffe2.python import core
  from pytext.common import Padding
  from pytext.config.component import create_loss
  from pytext.config.serialize import MissingValueError
  from pytext.data.utils import Vocabulary
  from pytext.loss import AUCPRHingeLoss, BinaryCrossEntropyLoss, CrossEntropyLoss, KLDivergenceBCELoss, KLDivergenceCELoss, LabelSmoothedCrossEntropyLoss, StructuredLoss, StructuredMarginLoss
  from pytext.models.crf import CRF
  from pytext.utils.label import get_label_weights
  from pytext.utils.usage import log_class_usage
  from .output_layer_base import OutputLayerBase
  from .utils import OutputLayerUtils
label_desc: Use image node_id 2 for calling the WordTaggingScores obj's underlying member method code with example usage: obj.forward(logits, context) and returns: _get_prediction_from_scores