class_docstr: Output layer for doc regression models. Currently only supports Mean Squared Error
  loss.
  
  Args:
      loss (MSELoss): config for MSE loss
      squash_to_unit_range (bool): whether to clamp the output to the range [0, 1],
          via a sigmoid.
docstr: Compute predictions and scores from the model (unlike in classification, where
  prediction = "most likely class" and scores = "log probs", here these are the
  same values). If `squash_to_unit_range` is True, fit prediction to [0, 1] via
  a sigmoid.
  
  Args:
      logit (torch.Tensor): Logits returned from the model.
  
  Returns:
      Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
docstr_tok: ["Compute","predictions","and","scores","from","the","model","(","unlike","in","classification",",","where","prediction","=","``","most","likely","class","''","and","scores","=","``","log","probs","''",",","here","these","are","the","same","values",")",".","If","`","squash_to_unit_range","`","is","True",",","fit","prediction","to","[","0",",","1","]","via","a","sigmoid",".","Args",":","logit","(","torch.Tensor",")",":","Logits","returned","from","the","model",".","Returns",":","Tuple","[","torch.Tensor",",","torch.Tensor","]",":","Model","prediction","and","scores","."]
code: def get_pred(self, logit, *args, **kwargs):
      """
      Compute predictions and scores from the model (unlike in classification, where
      prediction = "most likely class" and scores = "log probs", here these are the
      same values). If `squash_to_unit_range` is True, fit prediction to [0, 1] via
      a sigmoid.
  
      Args:
          logit (torch.Tensor): Logits returned from the model.
  
      Returns:
          Tuple[torch.Tensor, torch.Tensor]: Model prediction and scores.
      """
      prediction = logit.squeeze(dim=1)
      if self.squash_to_unit_range:
          prediction = torch.sigmoid(prediction)
      return prediction, prediction
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from pytext.config.component import create_loss
  from pytext.loss import MAELoss, MSELoss
  from pytext.utils.usage import log_class_usage
  from .output_layer_base import OutputLayerBase
label_desc: Use image node_id 4 for calling the RegressionOutputLayer obj's underlying member method code with example usage: obj.get_pred(logit) and returns: prediction,  prediction