class_docstr: Wrapper model to train multiple PyText models that share parameters.
  Designed to be used for multi-tasking when the tasks have disjoint datasets.
  
  Modules which have the same shared_module_key and type share parameters.
  Only need to configure the first such module in full in each case.
  
  Args:
      models (type): Dictionary of models of sub-tasks.
  
  Attributes:
      current_model (type): Current model to route the input batch to.
class_docstr_tok: ["Wrapper","model","to","train","multiple","PyText","models","that","share","parameters",".","Designed","to","be","used","for","multi-tasking","when","the","tasks","have","disjoint","datasets",".","Modules","which","have","the","same","shared_module_key","and","type","share","parameters",".","Only","need","to","configure","the","first","such","module","in","full","in","each","case",".","Args",":","models","(","type",")",":","Dictionary","of","models","of","sub-tasks",".","Attributes",":","current_model","(","type",")",":","Current","model","to","route","the","input","batch","to","."]
code: def get_pred(self, logits, targets=None, context=None, *args):
      return self.current_model.get_pred(
          logits, targets, context, *args
      )
  
file_path: pytext/pytext/models/disjoint_multitask_model.py
imports: from typing import List
  import torch
  import torch.nn
  from pytext.common.constants import BatchContext
  from pytext.models.model import Model
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 5 for calling the DisjointMultitaskModel obj's underlying member method code with example usage: obj.get_pred(logits, targets, context) and returns: self