code: def forward(  # type: ignore[override]
      ctx,
      A: Tensor,
      k: Optional[int] = None,
      B: Optional[Tensor] = None,
      X: Optional[Tensor] = None,
      n: Optional[int] = None,
      iK: Optional[Tensor] = None,
      niter: Optional[int] = None,
      tol: Optional[float] = None,
      largest: Optional[bool] = None,
      method: Optional[str] = None,
      tracker: None = None,
      ortho_iparams: Optional[Dict[str, int]] = None,
      ortho_fparams: Optional[Dict[str, float]] = None,
      ortho_bparams: Optional[Dict[str, bool]] = None,
  ) -> Tuple[Tensor, Tensor]:
      # makes sure that input is contiguous for efficiency.
      # Note: autograd does not support dense gradients for sparse input yet.
      A = A.contiguous() if (not A.is_sparse) else A
      if B is not None:
          B = B.contiguous() if (not B.is_sparse) else B
  
      D, U = _lobpcg(
          A,
          k,
          B,
          X,
          n,
          iK,
          niter,
          tol,
          largest,
          method,
          tracker,
          ortho_iparams,
          ortho_fparams,
          ortho_bparams,
      )
  
      ctx.save_for_backward(A, B, D, U)
      ctx.largest = largest
  
      return D, U
  
imports: from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor
  from .None import _linalg_utils
  from .overrides import handle_torch_function, has_torch_function
label_desc: Use image node_id 1 for calling the LOBPCGAutogradFunction obj's underlying member method code with example usage: obj.forward(ctx, A, k, B, X, n, iK, niter, tol, largest, method, tracker, ortho_iparams, ortho_fparams, ortho_bparams) and returns: D,  U