code: def _batch_transform(
      tapes: Sequence[QuantumTape],
      device: device_type,
      config: "qml.devices.ExecutionConfig",
      override_shots: Union[bool, int, Sequence[int]] = False,
      device_batch_transform: bool = True,
  ) -> Tuple[
      Sequence[QuantumTape], Callable, "qml.devices.ExecutionConfig"
  ]:
      """Apply the device batch transform unless requested not to.
  
      Args:
          tapes (Tuple[.QuantumTape]): batch of tapes to preprocess
          device (Device, devices.Device): the device that defines the required batch transformation
          config (qml.devices.ExecutionConfig): the config that characterizes the requested computation
          override_shots (int): The number of shots to use for the execution. If ``False``, then the
              number of shots on the device is used.
          device_batch_transform (bool): Whether to apply any batch transforms defined by the device
              (within :meth:`Device.batch_transform`) to each tape to be executed. The default behaviour
              of the device batch transform is to expand out Hamiltonian measurements into
              constituent terms if not supported on the device.
  
      Returns:
          Sequence[QuantumTape], Callable: The new batch of quantum scripts and the post processing
  
      """
      # TODO: Remove once old device are removed
      if device_batch_transform:
          dev_batch_transform = set_shots(device, override_shots)(
              device.batch_transform
          )
          return (
              *qml.transforms.map_batch_transform(
                  dev_batch_transform, tapes
              ),
              config,
          )
  
      def null_post_processing_fn(results):
          """A null post processing function used because the user requested not to use the device batch transform."""
          return results
  
      return tapes, null_post_processing_fn, config
  
file_path: pennylane/pennylane/workflow/execution.py
imports: import inspect
  import warnings
  from functools import wraps, partial
  from typing import Callable, Sequence, Optional, Union, Tuple
  import logging
  from cachetools import LRUCache, Cache
  import pennylane
  from pennylane.tape import QuantumTape
  from pennylane.typing import ResultBatch
  from .set_shots import set_shots
  from .jacobian_products import TransformJacobianProducts, DeviceDerivatives, DeviceJacobianProducts, LightningVJPs
label_desc: Use image node_id 3 for calling a global function with example usage: _batch_transform(tapes, device, config, override_shots, device_batch_transform) and returns: tapes,  null_post_processing_fn,  config, unknown,  config, results