code: def execute_compose(
      data: NdarrayOrTensor
      | Sequence[NdarrayOrTensor]
      | Mapping[Any, NdarrayOrTensor],
      transforms: Sequence[Any],
      map_items: bool = True,
      unpack_items: bool = False,
      start: int = 0,
      end: int | None = None,
      lazy: bool | None = False,
      overrides: dict | None = None,
      threading: bool = False,
      log_stats: bool | str = False,
  ) -> (
      NdarrayOrTensor
      | Sequence[NdarrayOrTensor]
      | Mapping[Any, NdarrayOrTensor]
  ):
      """
      ``execute_compose`` provides the implementation that the ``Compose`` class uses to execute a sequence
      of transforms. As well as being used by Compose, it can be used by subclasses of
      Compose and by code that doesn't have a Compose instance but needs to execute a
      sequence of transforms is if it were executed by Compose. It should only be used directly
      when it is not possible to use ``Compose.__call__`` to achieve the same goal.
      Args:
          data: a tensor-like object to be transformed
          transforms: a sequence of transforms to be carried out
          map_items: whether to apply transform to each item in the input `data` if `data` is a list or tuple.
              defaults to `True`.
          unpack_items: whether to unpack input `data` with `*` as parameters for the callable function of transform.
              defaults to `False`.
          start: the index of the first transform to be executed. If not set, this defaults to 0
          end: the index after the last transform to be executed. If set, the transform at index-1
              is the last transform that is executed. If this is not set, it defaults to len(transforms)
          lazy: whether to enable :ref:`lazy evaluation<lazy_resampling>` for lazy transforms. If False, transforms will be
              carried out on a transform by transform basis. If True, all lazy transforms will
              be executed by accumulating changes and resampling as few times as possible.
          overrides: this optional parameter allows you to specify a dictionary of parameters that should be overridden
              when executing a pipeline. These each parameter that is compatible with a given transform is then applied
              to that transform before it is executed. Note that overrides are currently only applied when
              :ref:`lazy evaluation<lazy_resampling>` is enabled for the pipeline or a given transform. If lazy is False
              they are ignored. Currently supported args are:
              {``"mode"``, ``"padding_mode"``, ``"dtype"``, ``"align_corners"``, ``"resample_mode"``, ``device``}.
          threading: whether executing is happening in a threaded environment. If set, copies are made
              of transforms that have the ``RandomizedTrait`` interface.
          log_stats: this optional parameter allows you to specify a logger by name for logging of pipeline execution.
              Setting this to False disables logging. Setting it to True enables logging to the default loggers.
              Setting a string overrides the logger name to which logging is performed.
  
      Returns:
          A tensorlike, sequence of tensorlikes or dict of tensorlists containing the result of running
          `data`` through the sequence of ``transforms``.
      """
      end_ = len(transforms) if end is None else end
      if start is None:
          raise ValueError(f"'start' ({start}) cannot be None")
      if start < 0:
          raise ValueError(f"'start' ({start}) cannot be less than 0")
      if start > end_:
          raise ValueError(
              f"'start' ({start}) must be less than 'end' ({end_})"
          )
      if end_ > len(transforms):
          raise ValueError(
              f"'end' ({end_}) must be less than or equal to the transform count ({len(transforms)}"
          )
  
      # no-op if the range is empty
      if start == end:
          return data
  
      for _transform in transforms[start:end]:
          if threading:
              _transform = (
                  deepcopy(_transform)
                  if isinstance(_transform, ThreadUnsafe)
                  else _transform
              )
          data = apply_transform(
              _transform,
              data,
              map_items,
              unpack_items,
              lazy=lazy,
              overrides=overrides,
              log_stats=log_stats,
          )
      data = apply_pending_transforms(
          data, None, overrides, logger_name=log_stats
      )
      return data
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Callable, Mapping, Sequence
  from copy import deepcopy
  from typing import Any
  import numpy
  import monai
  from monai.apps.utils import get_logger
  from monai.config import NdarrayOrTensor
  from monai.transforms.inverse import InvertibleTransform
  from monai.transforms.lazy.functional import apply_pending_transforms
  from monai.transforms.traits import ThreadUnsafe
  from monai.transforms.transform import LazyTransform, MapTransform, Randomizable, RandomizableTransform, Transform, apply_transform
  from monai.utils import MAX_SEED, TraceKeys, TraceStatusKeys, ensure_tuple, get_seed
label_desc: Use image node_id 1 for calling a global function with example usage: execute_compose(data, transforms, map_items, unpack_items, start, end, lazy, overrides, threading, log_stats) and returns: data, data