code: def gradient(self, idx, inputs):
      self.model.zero_grad()
      X = [x.requires_grad_() for x in inputs]
      outputs = self.model(*X)
      selected = [val for val in outputs[:, idx]]
      grads = []
      if self.interim:
          interim_inputs = self.layer.target_input
          for idx, input in enumerate(interim_inputs):
              grad = torch.autograd.grad(
                  selected,
                  input,
                  retain_graph=True
                  if idx + 1 < len(interim_inputs)
                  else None,
                  allow_unused=True,
              )[0]
              if grad is not None:
                  grad = grad.cpu().numpy()
              else:
                  grad = torch.zeros_like(X[idx]).cpu().numpy()
              grads.append(grad)
          del self.layer.target_input
          return grads, [
              i.detach().cpu().numpy() for i in interim_inputs
          ]
      else:
          for idx, x in enumerate(X):
              grad = torch.autograd.grad(
                  selected,
                  x,
                  retain_graph=True if idx + 1 < len(X) else None,
                  allow_unused=True,
              )[0]
              if grad is not None:
                  grad = grad.cpu().numpy()
              else:
                  grad = torch.zeros_like(X[idx]).cpu().numpy()
              grads.append(grad)
          return grads
  
file_path: shap/shap/explainers/_deep/deep_pytorch.py
imports: import warnings
  import numpy
  from packaging import version
  from .._explainer import Explainer
  from .deep_utils import _check_additivity
label_desc: Use image node_id 5 for calling the PyTorchDeep obj's underlying member method code with example usage: obj.gradient(idx, inputs) and returns: grads,  unknown, grads