def _autograd_grad()

in functorch/_src/eager_transforms.py [0:0]

• 18 lines of code
• 11 McCabe index (conditional complexity)

def _autograd_grad(outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True):
    if grad_outputs is None:
        diff_outputs = tuple(out for out in outputs if out.requires_grad)
    else:
        result = tuple((out, go) for out, go in zip(outputs, grad_outputs) if out.requires_grad)
        if len(result) == 0:
            diff_outputs, grad_outputs = (), ()
        else:
            diff_outputs, grad_outputs = zip(*result)
    if len(diff_outputs) == 0:
        return tuple(torch.zeros_like(inp) for inp in inputs)
    grad_inputs = torch.autograd.grad(diff_outputs, inputs, grad_outputs,
                                      retain_graph=retain_graph,
                                      create_graph=create_graph,
                                      allow_unused=True)
    grad_inputs = tuple(torch.zeros_like(inp) if gi is None else gi
                        for gi, inp in zip(grad_inputs, inputs))
    return grad_inputs