code: def tensor(
      data, dtype=torch.float32, device="cpu", requires_grad=False
  ):
      if isinstance(data, torch.Tensor):
          with torch.device(device):
              return (
                  data.clone()
                  .detach()
                  .type(dtype)
                  .requires_grad_(requires_grad)
              )
  
      if isinstance(data, np.ndarray):
          data = data.copy()
      return torch.tensor(
          data, dtype=dtype, device=device, requires_grad=requires_grad
      )
  
imports: from packaging.version import Version
  import numpy
  from .core import Backend, backend_types, backend_basic_math, backend_array
label_desc: Use image node_id 2 for calling the PyTorchBackend obj's underlying member method code with example usage: obj.tensor(data, dtype, device, requires_grad) and returns: torch, data