code: def _set_initial_v(
      vinit_type: str,
      dtype: torch.dtype,
      device: torch.device,
      batch_dims: Sequence,
      na: int,
      nguess: int,
      M: Optional[LinearOperator] = None,
  ) -> torch.Tensor:
      """Set the initial guess for the eigenvectors.
  
      Examples
      --------
      >>> import torch
      >>> vinit_type = "eye"
      >>> dtype = torch.float64
      >>> device = torch.device("cpu")
      >>> batch_dims = (2, 3)
      >>> na = 4
      >>> nguess = 2
      >>> M = None
      >>> V = _set_initial_v(vinit_type, dtype, device, batch_dims, na, nguess, M)
      >>> V
      tensor([[[[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]],
      <BLANKLINE>
               [[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]],
      <BLANKLINE>
               [[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]]],
      <BLANKLINE>
      <BLANKLINE>
              [[[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]],
      <BLANKLINE>
               [[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]],
      <BLANKLINE>
               [[1., 0.],
                [0., 1.],
                [0., 0.],
                [0., 0.]]]], dtype=torch.float64)
  
      Parameters
      ----------
      vinit_type: str
          Mode of the initial guess (``"randn"``, ``"rand"``, ``"eye"``)
      dtype: torch.dtype
          Data type of the initial guess.
      device: torch.device
          Device of the initial guess.
      batch_dims: Sequence
          Batch dimensions of the initial guess.
      na: int
          Number of basis functions.
      nguess: int
          Number of initial guesses.
      M: Optional[LinearOperator] (default None)
          The overlap matrix. If None, identity matrix is used.
  
      Returns
      -------
      V: torch.Tensor
          Initial guess for the eigenvectors.
  
      """
  
      torch.manual_seed(12421)
      if vinit_type == "eye":
          nbatch = functools.reduce(lambda x, y: x * y, batch_dims, 1)
          V = (
              torch.eye(na, nguess, dtype=dtype, device=device)
              .unsqueeze(0)
              .repeat(nbatch, 1, 1)
              .reshape(*batch_dims, na, nguess)
          )
      elif vinit_type == "randn":
          V = torch.randn(
              (*batch_dims, na, nguess), dtype=dtype, device=device
          )
      elif vinit_type == "random" or vinit_type == "rand":
          V = torch.rand(
              (*batch_dims, na, nguess), dtype=dtype, device=device
          )
      else:
          raise ValueError("Unknown v_init type: %s" % vinit_type)
  
      # orthogonalize V
      if isinstance(M, LinearOperator):
          V, R = tallqr(V, MV=M.mm(V))
      else:
          V, R = tallqr(V)
      return V
  
file_path: deepchem/deepchem/utils/differentiation_utils/symeig.py
imports: import torch
  from typing import Optional, Sequence, Tuple, Union
  from deepchem.utils.differentiation_utils import LinearOperator
  import functools
  from deepchem.utils.pytorch_utils import tallqr, to_fortran_order
  from deepchem.utils.differentiation_utils import get_bcasted_dims
label_desc: Use image node_id 3 for calling a global function with example usage: _set_initial_v(vinit_type, dtype, device, batch_dims, na, nguess, M) and returns: V