code: def _apply_transform(
      transform: Callable[..., ReturnType],
      data: Any,
      unpack_parameters: bool = False,
      lazy: bool | None = False,
      overrides: dict | None = None,
      logger_name: bool | str = False,
  ) -> ReturnType:
      """
      Perform a transform 'transform' on 'data', according to the other parameters specified.
  
      If `data` is a tuple and `unpack_parameters` is True, each parameter of `data` is unpacked
      as arguments to `transform`. Otherwise `data` is considered as single argument to `transform`.
  
      If 'lazy' is True, this method first checks whether it can execute this method lazily. If it
      can't, it will ensure that all pending lazy transforms on 'data' are applied before applying
      this 'transform' to it. If 'lazy' is True, and 'overrides' are provided, those overrides will
      be applied to the pending operations on 'data'. See ``Compose`` for more details on lazy
      resampling, which is an experimental feature for 1.2.
  
      Please note, this class is function is designed to be called by ``apply_transform``.
      In general, you should not need to make specific use of it unless you are implementing
      pipeline execution mechanisms.
  
      Args:
          transform: a callable to be used to transform `data`.
          data: the tensorlike or dictionary of tensorlikes to be executed on
          unpack_parameters: whether to unpack parameters for `transform`. Defaults to False.
          lazy: whether to enable lazy evaluation 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.
              See the :ref:`Lazy Resampling topic<lazy_resampling> for more information about lazy resampling.
          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 Resampling<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``}.
          logger_name: 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:
          ReturnType: The return type of `transform`.
      """
      from monai.transforms.lazy.functional import (
          apply_pending_transforms_in_order,
      )
  
      data = apply_pending_transforms_in_order(
          transform, data, lazy, overrides, logger_name
      )
  
      if isinstance(data, tuple) and unpack_parameters:
          return (
              transform(*data, lazy=lazy)
              if isinstance(transform, LazyTrait)
              else transform(*data)
          )
  
      return (
          transform(data, lazy=lazy)
          if isinstance(transform, LazyTrait)
          else transform(data)
      )
  
imports: from __future__ import annotations
  import logging
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Generator, Hashable, Iterable, Mapping
  from typing import Any, TypeVar
  import numpy
  import torch
  from monai import config, transforms
  from monai.config import KeysCollection
  from monai.data.meta_tensor import MetaTensor
  from monai.transforms.traits import LazyTrait, RandomizableTrait, ThreadUnsafe
  from monai.utils import MAX_SEED, ensure_tuple, first
  from monai.utils.enums import TransformBackends
  from monai.utils.misc import MONAIEnvVars
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_transform(transform, data, unpack_parameters, lazy, overrides, logger_name) and returns: unknown, unknown