code: def _register_torch_vjp():
      """
      Register the custom VJP for torch
      """
      # pylint: disable=import-outside-toplevel,abstract-method,arguments-differ
      import torch
  
      class _TorchFidelity(torch.autograd.Function):
          @staticmethod
          def forward(ctx, dm0, dm1):
              """Forward pass for _compute_fidelity"""
              fid = _compute_fidelity_vanilla(dm0, dm1)
              ctx.save_for_backward(dm0, dm1)
              return fid
  
          @staticmethod
          def backward(ctx, grad_out):
              """Backward pass for _compute_fidelity"""
              dm0, dm1 = ctx.saved_tensors
              return _compute_fidelity_grad(dm0, dm1, grad_out)
  
      ar.register_function(
          "torch", "compute_fidelity", _TorchFidelity.apply
      )
  
file_path: pennylane/pennylane/math/fidelity.py
imports: from functools import lru_cache
  import autograd
  import autoray
  import pennylane
  from .utils import cast
  from .quantum import _check_density_matrix, _check_state_vector
label_desc: Use image node_id 12 for calling a global function with example usage: _register_torch_vjp() and returns: fid, _compute_fidelity_grad