code: def jax_jvp_execute(
      tapes: Batch, execute_fn: ExecuteFn, jpc, device=None
  ):
      """Execute a batch of tapes with JAX parameters using JVP derivatives.
  
      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 Jacobian vector product (JVP)
              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):  # pragma: no cover
          logger.debug(
              "Entry with (tapes=%s, execute_fn=%s, jpc=%s)",
              tapes,
              execute_fn,
              jpc,
          )
  
      parameters = tuple(tuple(t.get_parameters()) for t in tapes)
  
      return _execute_jvp(
          parameters, _NonPytreeWrapper(tuple(tapes)), execute_fn, jpc
      )
  
file_path: pennylane/pennylane/workflow/interfaces/jax.py
imports: import logging
  from typing import Tuple, Callable
  import dataclasses
  import jax
  import jax.numpy
  import pennylane
  from pennylane.transforms import convert_to_numpy_parameters
  from pennylane.typing import ResultBatch
label_desc: Use image node_id 7 for calling a global function with example usage: jax_jvp_execute(tapes, execute_fn, jpc, device) and returns: _execute_jvp