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.
  
  :param tuple inputs: list of original text
docstr_tok: ["Get","gradient","of","loss","with","respect","to","input","tokens",".",":","param","tuple","inputs",":","list","of","original","text"]
code: def get_model_grad(self, *inputs):
      r"""
      Get gradient of loss with respect to input tokens.
  
      :param tuple inputs: list of original text
  
      """
      raise NotImplementedError()
  
file_path: textflint/textflint/input/model/flint_model/flint_model.py
imports: from abc import ABC
  import numpy
  from ..metrics.metrics import accuracy_score
  from ..metrics.metrics import POSMetric
  from ..metrics.nerspanmetric import NERSpanMetric
label_desc: Use image node_id 5 for calling the FlintModel obj's underlying member method code with example usage: obj.get_model_grad() without return types