class_docstr: A model wrapper queries a model with a list of text inputs.
  
  Classification-based models return a list of lists, where each
  sublist represents the model's scores for a given input.
  
  Text-to-text models return a list of strings, where each string is
  the output – like a translation or summarization – for a given
  input.
class_docstr_tok: ["A","model","wrapper","queries","a","model","with","a","list","of","text","inputs",".","Classification-based","models","return","a","list","of","lists",",","where","each","sublist","represents","the","model","'s","scores","for","a","given","input",".","Text-to-text","models","return","a","list","of","strings",",","where","each","string","is","the","output","\u2013","like","a","translation","or","summarization","\u2013","for","a","given","input","."]
docstr: Get gradient of loss with respect to input tokens.
docstr_tok: ["Get","gradient","of","loss","with","respect","to","input","tokens","."]
code: def get_grad(self, text_input):
      """Get gradient of loss with respect to input tokens."""
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
label_desc: Use image node_id 2 for calling the ModelWrapper obj's underlying member method code with example usage: obj.get_grad(text_input) without return types