code: def cache_execute(
      fn: Callable,
      cache,
      pass_kwargs=False,
      return_tuple=True,
      expand_fn=None,
  ):
      """Decorator that adds caching to a function that executes
      multiple tapes on a device.
  
      This decorator makes use of :attr:`.QuantumTape.hash` to identify
      unique tapes.
  
      - If a tape does not match a hash in the cache, then the tape
        has not been previously executed. It is executed, and the result
        added to the cache.
  
      - If a tape matches a hash in the cache, then the tape has been previously
        executed. The corresponding cached result is
        extracted, and the tape is not passed to the execution function.
  
      - Finally, there might be the case where one or more tapes in the current
        set of tapes to be executed are identical and thus share a hash. If this is the case,
        duplicates are removed, to avoid redundant evaluations.
  
      Args:
          fn (callable): The execution function to add caching to.
              This function should have the signature ``fn(tapes, **kwargs)``,
              and it should return ``list[tensor_like]``, with the
              same length as the input ``tapes``.
          cache (None or dict or Cache or bool): The cache to use. If ``None``,
              caching will not occur.
          pass_kwargs (bool): If ``True``, keyword arguments passed to the
              wrapped function will be passed directly to ``fn``. If ``False``,
              they will be ignored.
          return_tuple (bool): If ``True``, the output of ``fn`` is returned
              as a tuple ``(fn_ouput, [])``, to match the output of execution functions
              that also return gradients.
  
      Returns:
          function: a wrapped version of the execution function ``fn`` with caching
          support
      """
      if logger.isEnabledFor(logging.DEBUG):
          logger.debug(
              "Entry with args=(fn=%s, cache=%s, pass_kwargs=%s, return_tuple=%s, expand_fn=%s) called by=%s",
              fn
              if not (
                  logger.isEnabledFor(qml.logging.TRACE)
                  and inspect.isfunction(fn)
              )
              else "\n" + inspect.getsource(fn),
              cache,
              pass_kwargs,
              return_tuple,
              expand_fn
              if not (
                  logger.isEnabledFor(qml.logging.TRACE)
                  and inspect.isfunction(expand_fn)
              )
              else "\n" + inspect.getsource(expand_fn) + "\n",
              "::L".join(
                  str(i)
                  for i in inspect.getouterframes(
                      inspect.currentframe(), 2
                  )[1][1:3]
              ),
          )
  
      if expand_fn is not None:
          original_fn = fn
  
          def fn(
              tapes: Sequence[QuantumTape], **kwargs
          ):  # pylint: disable=function-redefined
              tapes = [expand_fn(tape) for tape in tapes]
              return original_fn(tapes, **kwargs)
  
      @wraps(fn)
      def wrapper(tapes: Sequence[QuantumTape], **kwargs):
          if not pass_kwargs:
              kwargs = {}
  
          if cache is None or (isinstance(cache, bool) and not cache):
              # No caching. Simply execute the execution function
              # and return the results.
  
              # must convert to list as new device interface returns tuples
              res = list(fn(tapes, **kwargs))
              return (res, []) if return_tuple else res
  
          execution_tapes = {}
          cached_results = {}
          hashes = {}
          repeated = {}
  
          for i, tape in enumerate(tapes):
              h = tape.hash
  
              if h in hashes.values():
                  # Tape already exists within ``tapes``. Determine the
                  # index of the first occurrence of the tape, store this,
                  # and continue to the next iteration.
                  idx = list(hashes.keys())[
                      list(hashes.values()).index(h)
                  ]
                  repeated[i] = idx
                  continue
  
              hashes[i] = h
  
              if hashes[i] in cache:
                  # Tape exists within the cache, store the cached result
                  cached_results[i] = cache[hashes[i]]
                  if tape.shots and getattr(
                      cache, "_persistent_cache", True
                  ):
                      warnings.warn(
                          "Cached execution with finite shots detected!\n"
                          "Note that samples as well as all noisy quantities computed via sampling "
                          "will be identical across executions. This situation arises where tapes "
                          "are executed with identical operations, measurements, and parameters.\n"
                          "To avoid this behavior, provide 'cache=False' to the QNode or execution "
                          "function.",
                          UserWarning,
                      )
              else:
                  # Tape does not exist within the cache, store the tape
                  # for execution via the execution function.
                  execution_tapes[i] = tape
  
          # if there are no execution tapes, simply return!
          if not execution_tapes:
              if not repeated:
                  res = list(cached_results.values())
                  return (res, []) if return_tuple else res
  
          else:
              # execute all unique tapes that do not exist in the cache
              # convert to list as new device interface returns a tuple
              res = list(fn(tuple(execution_tapes.values()), **kwargs))
  
          final_res = []
  
          for i, tape in enumerate(tapes):
              if i in cached_results:
                  # insert cached results into the results vector
                  final_res.append(cached_results[i])
  
              elif i in repeated:
                  # insert repeated results into the results vector
                  final_res.append(final_res[repeated[i]])
  
              else:
                  # insert evaluated results into the results vector
                  r = res.pop(0)
                  final_res.append(r)
                  cache[hashes[i]] = r
  
          return (final_res, []) if return_tuple else final_res
  
      wrapper.fn = fn
      return wrapper
  
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 6 for calling a global function with example usage: cache_execute(fn, cache, pass_kwargs, return_tuple, expand_fn) and returns: wrapper, unknown, original_fn, unknown, unknown