code: def manage_advanced_indexing(
      idx: Union[slice, int, Iterable[int]],
      single_element_getter: Callable[[int], X],
      max_length: int,
      collate_fn: Callable[[Iterable[X]], T],
  ) -> Union[X, T]:
      """
      Utility function used to manage the advanced indexing and slicing.
  
      If more than a pattern is selected, the X and Y values will be merged
      in two separate torch Tensor objects using the stack operation.
  
      :param idx: Either an in, a slice object or a list (including ndarrays and
          torch Tensors) of indexes.
      :param single_element_getter: A callable used to obtain a single element
          given its int index.
      :param max_length: The maximum sequence length.
      :param collate_fn: The function to use to create a batch of data from
          single elements.
      :return: A tuple consisting of two tensors containing the X and Y values
          of the patterns addressed by the idx parameter.
      """
      indexes_iterator: Iterable[int] = slice_alike_object_to_indices(
          slice_alike_object=idx, max_length=max_length
      )
  
      elements: List[X] = []
      for single_idx in indexes_iterator:
          single_element = single_element_getter(int(single_idx))
          elements.append(single_element)
  
      if len(elements) == 1:
          return elements[0]
  
      return collate_fn(elements)
  
imports: from abc import ABC, abstractmethod
  import bisect
  import copy
  from typing import Iterator, overload
  from typing_extensions import final
  import numpy
  from numpy import ndarray
  from torch import Tensor
  from torch.utils.data import Subset
  from .dataset_definitions import IDatasetWithTargets
  from typing import Sequence, List, Iterable, Union, Optional, TypeVar, Callable, Generic
label_desc: Use image node_id 2 for calling a global function with example usage: manage_advanced_indexing(idx, single_element_getter, max_length, collate_fn) and returns: collate_fn, elements