code: def vjp(
      ans,
      parameters,
      tapes,
      execute_fn,
      jpc,
  ):
      """Returns the vector-Jacobian product operator for a batch of quantum tapes.
  
      Args:
          ans (array): the result of the batch tape execution
          parameters (list[list[Any]]): Nested list of the quantum tape parameters.
              This argument should be generated from the provided list of tapes.
          tapes (Sequence[.QuantumTape]): batch of tapes to execute
          execute_fn (Callable[[Sequence[.QuantumTape]], ResultBatch]): a function that turns a batch of circuits into results
          jpc (JacobianProductCalculator): a class that can compute the vector Jacobian product (VJP)
              for the input tapes.
  
  
      Returns:
          function: this function accepts the backpropagation
          gradient output vector, and computes the vector-Jacobian product
      """
  
      def grad_fn(dy):
          """Returns the vector-Jacobian product with given
          parameter values and output gradient dy"""
          vjps = jpc.compute_vjp(tapes, dy)
          return tuple(
              qml.math.to_numpy(v, max_depth=1)
              if isinstance(v, ArrayBox)
              else v
              for v in vjps
          )
  
      return grad_fn
  
imports: import logging
  from typing import Tuple, Callable
  import autograd
  from autograd.numpy.numpy_boxes import ArrayBox
  import pennylane
label_desc: Use image node_id 3 for calling a global function with example usage: vjp(ans, parameters, tapes, execute_fn, jpc) and returns: grad_fn, tuple