class_docstr: BaseGrid is a class that regulates the integration points over the spatial
  dimensions.
  
  Examples
  --------
  >>> import torch
  >>> from deepchem.utils.dft_utils import BaseGrid
  >>> class Grid(BaseGrid):
  ...     def __init__(self):
  ...         super(Grid, self).__init__()
  ...         self.ngrid = 10
  ...         self.ndim = 3
  ...         self.dvolume = torch.ones(self.ngrid, dtype=self.dtype, device=self.device)
  ...         self.rgrid = torch.ones((self.ngrid, self.ndim), dtype=self.dtype, device=self.device)
  ...     def get_dvolume(self):
  ...         return self.dvolume
  ...     def get_rgrid(self):
  ...         return self.rgrid
  >>> grid = Grid()
  >>> grid.get_dvolume()
  tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
  >>> grid.get_rgrid()
  tensor([[1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.],
          [1., 1., 1.]])
  
  References
  ----------
  Kasim, Muhammad F., and Sam M. Vinko. "Learning the exchange-correlation
  functional from nature with fully differentiable density functional theory."
  Physical Review Letters 127.12 (2021): 126403.
  https://github.com/diffqc/dqc/blob/0fe821fc92cb3457fb14f6dff0c223641c514ddb/dqc/grid/base_grid.py
docstr: dtype of the grid points.
  
  Returns
  -------
  torch.dtype
      dtype of the grid points
code: def dtype(self) -> torch.dtype:
      """dtype of the grid points.
  
      Returns
      -------
      torch.dtype
          dtype of the grid points
  
      """
      pass
  
file_path: deepchem/deepchem/utils/dft_utils/grid/base_grid.py
imports: from deepchem.utils.differentiation_utils import EditableModule
  import torch
  from abc import abstractmethod, abstractproperty
  from typing import List
label_desc: Use image node_id 1 for calling the BaseGrid obj's underlying member method code with example usage: obj.dtype() without return types