class_docstr: Base class for all output layers in PyText. The responsibilities of this layer are
  
      1. Implement how loss is computed from logits and targets.
      2. Implement how to get predictions from logits.
      3. Implement the Caffe2 operator for performing the above tasks. This is
          used when PyText exports PyTorch model to Caffe2.
  
  Args:
      loss_fn (type): The loss function object to use for computing loss.
          Defaults to None.
  
  Attributes:
      loss_fn: The loss function object to use for computing loss.
docstr: Compute and return prediction and scores from the model.
  
  Args:
      logit (torch.Tensor): Logits returned :class:`~pytext.models.Model`.
      targets (Optional[torch.Tensor]): True label/target. Only used by
          :class:`~pytext.models.output_layer.LMOutputLayer`. Defaults to None.
      context (Optional[Dict[str, Any]]): Context is a dictionary of items
          that's passed as additional metadata by the
          :class:`~pytext.data.DataHandler`. Defaults to None.
  
  Returns:
      Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
code: def get_pred(
      self,
      logit: torch.Tensor,
      targets: Optional[torch.Tensor] = None,
      context: Optional[Dict[str, Any]] = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Compute and return prediction and scores from the model.
  
      Args:
          logit (torch.Tensor): Logits returned :class:`~pytext.models.Model`.
          targets (Optional[torch.Tensor]): True label/target. Only used by
              :class:`~pytext.models.output_layer.LMOutputLayer`. Defaults to None.
          context (Optional[Dict[str, Any]]): Context is a dictionary of items
              that's passed as additional metadata by the
              :class:`~pytext.data.DataHandler`. Defaults to None.
  
      Returns:
          Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
  
      """
      return logit, None
  
file_path: pytext/pytext/models/output_layers/output_layer_base.py
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from caffe2.python import core
  from pytext.loss import Loss
  from pytext.models.module import Module
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 3 for calling the OutputLayerBase obj's underlying member method code with example usage: obj.get_pred(logit, targets, context) and returns: logit,  None