class_docstr: BaseDF represents the density fitting object used in calculating the
  electron repulsion (and xc energy) in Hamiltonian.
  
  Density fitting in density functional theory (DFT) is a technique used to
  reduce the computational cost of evaluating electron repulsion integrals.
  In DFT, the key quantity is the electron density rather than the wave
  function, and the electron repulsion integrals involve four-electron
  interactions, making them computationally demanding.
  
  Density fitting exploits the fact that many-electron integrals can be
  expressed as a sum of two-electron integrals involving auxiliary basis
  functions. By approximating these auxiliary basis functions, often referred
  to as fitting functions, the computational cost can be significantly reduced.
  
  Examples
  --------
  >>> from deepchem.utils.dft_utils import BaseDF
  >>> import torch
  >>> class MyDF(BaseDF):
  ...     def __init__(self):
  ...         super(MyDF, self).__init__()
  ...     def get_j2c(self):
  ...         return torch.ones((3, 3))
  ...     def get_j3c(self):
  ...         return torch.ones((3, 3, 3))
  >>> df = MyDF()
  >>> df.get_j2c()
  tensor([[1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.]])
docstr: This method should list tensor names that affect the output of the
  method with name indicated in ``methodname``.
  
  Parameters
  ---------
  methodname: str
      The name of the method of the class.
  prefix: str (default="")
      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.
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``.
  
      Parameters
      ---------
      methodname: str
          The name of the method of the class.
      prefix: str (default="")
          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.
  
      """
      pass
  
file_path: deepchem/deepchem/utils/dft_utils/df/base_df.py
imports: from __future__ import annotations
  from abc import abstractmethod, abstractproperty
  from typing import List
  import torch
  from deepchem.utils.differentiation_utils import EditableModule, LinearOperator
label_desc: Use image node_id 5 for calling the BaseDF obj's underlying member method code with example usage: obj.getparamnames(methodname, prefix) without return types