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: This method should list tensor names that affect the output of the
  method with name indicated in ``methodname``.
  If the ``methodname`` is not on the list in this function, it should
  raise ``KeyError``.
  
  Parameters
  ---------
  methodname: str
      The name of the method of the class.
  prefix: str
      The prefix to be appended in front of the parameters name.
      This usually contains the dots.
  
  Returns
  -------
  List[str]
      Sequence of name of parameters affecting the output of the method.
  
  Raises
  ------
  KeyError
      If the list in this function does not contain ``methodname``.
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code: def getparamnames(
      self, methodname: str, prefix: str = ""
  ) -> List[str]:
      """
      This method should list tensor names that affect the output of the
      method with name indicated in ``methodname``.
      If the ``methodname`` is not on the list in this function, it should
      raise ``KeyError``.
  
      Parameters
      ---------
      methodname: str
          The name of the method of the class.
      prefix: str
          The prefix to be appended in front of the parameters name.
          This usually contains the dots.
  
      Returns
      -------
      List[str]
          Sequence of name of parameters affecting the output of the method.
  
      Raises
      ------
      KeyError
          If the list in this function does not contain ``methodname``.
  
      """
      pass
  
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 4 for calling the EditableModule obj's underlying member method code with example usage: obj.getparamnames(methodname, prefix) without return types