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: Returns a list of tensor parameters used in the object's operations.
  Requires the ``getparamnames`` method to be implemented.
  
  Parameters
  ----------
  methodname: str
      The name of the method of the class.
  
  Returns
  -------
  Sequence[torch.Tensor]
      Sequence of tensors that are involved in the specified method of the
      object.
docstr_tok: ["Returns","a","list","of","tensor","parameters","used","in","the","object","'s","operations",".","Requires","the","``","getparamnames","``","method","to","be","implemented",".","Parameters","--","--","--","--","--","methodname",":","str","The","name","of","the","method","of","the","class",".","Returns","--","--","--","-","Sequence","[","torch.Tensor","]","Sequence","of","tensors","that","are","involved","in","the","specified","method","of","the","object","."]
code: def getparams(self, methodname: str) -> Sequence[torch.Tensor]:
      """Returns a list of tensor parameters used in the object's operations.
      Requires the ``getparamnames`` method to be implemented.
  
      Parameters
      ----------
      methodname: str
          The name of the method of the class.
  
      Returns
      -------
      Sequence[torch.Tensor]
          Sequence of tensors that are involved in the specified method of the
          object.
  
      """
  
      paramnames = self.cached_getparamnames(methodname)
      return [get_attr(self, name) for name in paramnames]
  
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 1 for calling the EditableModule obj's underlying member method code with example usage: obj.getparams(methodname) and returns: unknown