class_docstr: Output layer for word tagging models. It supports `CrossEntropyLoss` per word.
  
  Args:
      loss_fn (CrossEntropyLoss): Cross-entropy loss component. Defaults to None.
  
  Attributes:
      loss_fn: Cross-entropy loss component.
class_docstr_tok: ["Output","layer","for","word","tagging","models",".","It","supports","`","CrossEntropyLoss","`","per","word",".","Args",":","loss_fn","(","CrossEntropyLoss",")",":","Cross-entropy","loss","component",".","Defaults","to","None",".","Attributes",":","loss_fn",":","Cross-entropy","loss","component","."]
docstr: Compute and return prediction and scores from the model.
  Prediction is computed using argmax over the word label/target space.
  Scores are softmax scores over the model logits.
  
  Args:
      logit (torch.Tensor): Logits returned
          :class:`~pytext.models.word_model.WordTaggingModel`.
  
  Returns:
      Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
docstr_tok: ["Compute","and","return","prediction","and","scores","from","the","model",".","Prediction","is","computed","using","argmax","over","the","word","label\/target","space",".","Scores","are","softmax","scores","over","the","model","logits",".","Args",":","logit","(","torch.Tensor",")",":","Logits","returned",":","class",":","`","~pytext.models.word_model.WordTaggingModel","`",".","Returns",":","Tuple","[","torch.Tensor",",","torch.Tensor","]",":","Model","prediction","and","scores","."]
code: def get_pred(
      self, logit: torch.Tensor, *args, **kwargs
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Compute and return prediction and scores from the model.
      Prediction is computed using argmax over the word label/target space.
      Scores are softmax scores over the model logits.
  
      Args:
          logit (torch.Tensor): Logits returned
              :class:`~pytext.models.word_model.WordTaggingModel`.
  
      Returns:
          Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
  
      """
      preds = torch.max(logit, 2)[1]
      scores = F.log_softmax(logit, 2)
      return preds, scores
  
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 3 for calling the WordTaggingOutputLayer obj's underlying member method code with example usage: obj.get_pred(logit) and returns: preds,  scores