class_docstr: Calculates VJPs natively using lightning.qubit.
  
  Args:
      device (LightningBase): a device in the lightning ecosystem (``lightning.qubit``, ``lightning.gpu``, ``lightning.kokkos``.)
      gradient_kwargs (Optional[dict]):  Any gradient options.
  
  >>> dev = qml.device('lightning.qubit', wires=5)
  >>> jpc = LightningVJPs(dev, gradient_kwargs={"use_device_state": True, "method": "adjoint_jacobian"})
  >>> tape = qml.tape.QuantumScript([qml.RX(1.2, wires=0)], [qml.expval(qml.PauliZ(0))])
  >>> dev.batch_execute((tape,))
  [array(0.36235775)]
  >>> jpc.compute_vjp((tape,), (0.5,) )
  ((array(-0.46601954),),)
  >>> -0.5 * np.sin(1.2)
  -0.46601954298361314
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code: def __repr__(self):
      return f"<LightningVJPs: {self._device.short_name}, {self._gradient_kwargs}>"
  
imports: import abc
  from functools import partial
  import inspect
  import logging
  from typing import Tuple, Callable, Optional, Union
  from cachetools import LRUCache
  import numpy
  import pennylane
  from pennylane.tape import QuantumScript
  from pennylane.typing import ResultBatch, TensorLike
label_desc: Use image node_id 1 for calling the LightningVJPs obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str