class_docstr: EditableModule is a base class to enable classes that it inherits be
  converted to pure functions for higher order derivatives purpose.
  
  Usage
  -----
  To use this class, the user must implement the ``getparamnames`` method
  which returns a list of tensor names that affect the output of the method
  with name indicated in ``methodname``.
  
  Used in:
  
  - Classes of Density Functional Theory (DFT).
  
  - It can also be used in other classes that need to be converted to pure
    functions for higher order derivatives purpose.
  
  Examples
  --------
  >>> import torch
  >>> from deepchem.utils.differentiation_utils import EditableModule
  >>> class A(EditableModule):
  ...     def __init__(self, a):
  ...         self.b = a*a
  ...
  ...     def mult(self, x):
  ...         return self.b * x
  ...
  ...     def getparamnames(self, methodname, prefix=""):
  ...         if methodname == "mult":
  ...             return [prefix+"b"]
  ...         else:
  ...             raise KeyError()
  >>> a = torch.tensor(2.0).requires_grad_()
  >>> x = torch.tensor(0.4).requires_grad_()
  >>> alpha = A(a)
  >>> alpha.mult(x)
  tensor(1.6000, grad_fn=<MulBackward0>)
  >>> alpha.getparamnames("mult")
  ['b']
  >>> alpha.assertparams(alpha.mult, x)
  "mult" method check done
docstr: Set the input parameters to the object's parameters to make a copy of
  the operations. The input parameters are unique parameters, i.e. they
  are not necessarily the same tensors as the object's parameters.
  
  Note: This function can only be run after running getuniqueparams.
  
  Parameters
  ----------
  methodname: str
      The name of the method of the class.
  *uniqueparams:
      The parameters to be set to the object's parameters. The number of
      parameters must be the same as the number of unique parameters
      returned by ``getuniqueparams``.
  
  Returns
  -------
  int
      The number of parameters that are set to the object's parameters.
docstr_tok: ["Set","the","input","parameters","to","the","object","'s","parameters","to","make","a","copy","of","the","operations",".","The","input","parameters","are","unique","parameters",",","i.e",".","they","are","not","necessarily","the","same","tensors","as","the","object","'s","parameters",".","Note",":","This","function","can","only","be","run","after","running","getuniqueparams",".","Parameters","--","--","--","--","--","methodname",":","str","The","name","of","the","method","of","the","class",".","*","uniqueparams",":","The","parameters","to","be","set","to","the","object","'s","parameters",".","The","number","of","parameters","must","be","the","same","as","the","number","of","unique","parameters","returned","by","``","getuniqueparams","``",".","Returns","--","--","--","-","int","The","number","of","parameters","that","are","set","to","the","object","'s","parameters","."]
code: def setuniqueparams(self, methodname: str, *uniqueparams) -> int:
      """Set the input parameters to the object's parameters to make a copy of
      the operations. The input parameters are unique parameters, i.e. they
      are not necessarily the same tensors as the object's parameters.
  
      Note: This function can only be run after running getuniqueparams.
  
      Parameters
      ----------
      methodname: str
          The name of the method of the class.
      *uniqueparams:
          The parameters to be set to the object's parameters. The number of
          parameters must be the same as the number of unique parameters
          returned by ``getuniqueparams``.
  
      Returns
      -------
      int
          The number of parameters that are set to the object's parameters.
  
      """
      nparams = self._number_of_params[methodname]
      allparams = [None for _ in range(nparams)]
      maps = self._unique_params_maps[methodname]
  
      for j in range(len(uniqueparams)):
          jmap = maps[j]
          p = uniqueparams[j]
          for i in jmap:
              allparams[i] = p
  
      return self.setparams(methodname, *allparams)
  
imports: import inspect
  import warnings
  from abc import abstractmethod
  import copy
  import torch
  from typing import Sequence, Union, Dict, List, Callable, Any
  from deepchem.utils.attribute_utils import get_attr, set_attr, del_attr
label_desc: Use image node_id 6 for calling the EditableModule obj's underlying member method code with example usage: obj.setuniqueparams(methodname) and returns: self