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","."]
code: def torchscript_predictions(self):
      return jit.script(WordTaggingScores(self.target_names))
  
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 5 for calling the WordTaggingOutputLayer obj's underlying member method code with example usage: obj.torchscript_predictions() and returns: jit