code: def execute(tapes, execute_fn, jpc, device=None):
      """Execute a batch of tapes with Torch parameters on a device.
  
      Args:
          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 for the input tapes.
  
      Returns:
          TensorLike: A nested tuple of tape results. Each element in
          the returned tuple corresponds in order to the provided tapes.
      """
      if logger.isEnabledFor(logging.DEBUG):
          logger.debug(
              "Entry with args=(tapes=%s, execute_fn=%s, jpc=%s",
              tapes,
              f"\n{inspect.getsource(execute_fn)}\n"
              if logger.isEnabledFor(qml.logging.TRACE)
              else execute_fn,
              jpc,
          )
  
      parameters = []
      for tape in tapes:
          # set the trainable parameters
          params = tape.get_parameters(trainable_only=False)
          tape.trainable_params = qml.math.get_trainable_indices(params)
          parameters.extend(tape.get_parameters())
  
      kwargs = {
          "tapes": tuple(tapes),
          "execute_fn": execute_fn,
          "jpc": jpc,
      }
  
      return ExecuteTapes.apply(kwargs, *parameters)
  
file_path: pennylane/pennylane/workflow/interfaces/torch.py
imports: import inspect
  import logging
  import numpy
  import torch
  import torch.utils._pytree
  import pennylane
label_desc: Use image node_id 3 for calling a global function with example usage: execute(tapes, execute_fn, jpc, device) and returns: ExecuteTapes