code: def tallqr(V, MV=None):
      """QR decomposition for tall and skinny matrix.
  
      Examples
      --------
      >>> import torch
      >>> from deepchem.utils.pytorch_utils import tallqr
      >>> V = torch.randn(3, 2)
      >>> Q, R = tallqr(V)
      >>> Q.shape
      torch.Size([3, 2])
      >>> R.shape
      torch.Size([2, 2])
      >>> torch.allclose(Q @ R, V)
      True
  
      Parameters
      ----------
      V: torch.Tensor
          V is a matrix to be decomposed. (*BV, na, nguess)
      MV: torch.Tensor
          (*BM, na, nguess) where M is the basis to make Q M-orthogonal
          if MV is None, then MV=V (default=None)
  
      Returns
      -------
      Q: torch.Tensor
          The Orthogonal Part. Shape: (*BV, na, nguess)
      R: torch.Tensor
          The (*BM, nguess, nguess) where M is the basis to make Q M-orthogonal
  
      """
      if MV is None:
          MV = V
      VTV = torch.matmul(
          V.transpose(-2, -1), MV
      )  # (*BMV, nguess, nguess)
      R = (
          torch.linalg.cholesky(VTV.transpose(-2, -1).conj())
          .transpose(-2, -1)
          .conj()
      )  # (*BMV, nguess, nguess)
      Rinv = torch.inverse(R)  # (*BMV, nguess, nguess)
      Q = torch.matmul(V, Rinv)
      return Q, R
  
file_path: deepchem/deepchem/utils/pytorch_utils.py
imports: import scipy
  import torch
  from typing import Callable, Union, List, Generator, Tuple
label_desc: Use image node_id 7 for calling a global function with example usage: tallqr(V, MV) and returns: Q,  R