class_docstr: Data structure to store different values for spin-up and spin-down electrons.
  
  Examples
  --------
  >>> import torch
  >>> from deepchem.utils.dft_utils import SpinParam
  >>> dens_u = torch.ones(1)
  >>> dens_d = torch.zeros(1)
  >>> sp = SpinParam(u=dens_u, d=dens_d)
  >>> sp.u
  tensor([1.])
  >>> sp.sum()
  tensor([1.])
  >>> sp.reduce(torch.multiply)
  tensor([0.])
docstr: Initialize the SpinParam object.
  
  Parameters
  ----------
  u: any type
      The parameters that corresponds to the spin-up electrons.
  d: any type
      The parameters that corresponds to the spin-down electrons.
docstr_tok: ["Initialize","the","SpinParam","object",".","Parameters","--","--","--","--","--","u",":","any","type","The","parameters","that","corresponds","to","the","spin-up","electrons",".","d",":","any","type","The","parameters","that","corresponds","to","the","spin-down","electrons","."]
code: def __init__(self, u: T, d: T):
      """Initialize the SpinParam object.
  
      Parameters
      ----------
      u: any type
          The parameters that corresponds to the spin-up electrons.
      d: any type
          The parameters that corresponds to the spin-down electrons.
  
      """
      self.u = u
      self.d = d
  
imports: from typing import Union, TypeVar, Generic, Optional, Callable, List, Dict
  from dataclasses import dataclass
  import torch
  import numpy
  from deepchem.utils import gaussian_integral
label_desc: Use image node_id 1 to create a new SpinParam object from inherited base classes: Generic with example: obj = SpinParam(u, d)