code: def contiguous_ints_or_floats(numbers: np.ndarray) -> bool:
      """
      Check for continuity in the given list of numbers.
  
      Check whether the given list of numbers contains
      contiguous integers or contiguous integer-like
      floats. For example, [1, 2, 3] or [4.0, 5.0, 6.0]
      are both contiguous but [1.1, 1.2, 1.3] is not.
  
      Parameters
      ----------
      numbers : numpy.ndarray
          The numbers we want to check.
  
      Returns
      -------
      bool
          ``True`` if the numbers are contiguous integers
          or contiguous integer-like floats (1.0, 2.0, etc.),
          ``False`` otherwise.
  
      Raises
      ------
      TypeError
          If ``numbers`` does not contain integers or floating point values.
      ValueError
          If ``numbers`` is empty.
      """
      try:
          # make sure that number is not empty
          assert len(numbers) > 0
  
          # first check that the numbers are all integers
          # or integer-like floats (e.g., 1.0, 2.0 etc.)
          ints_or_int_like_floats = np.all(np.mod(numbers, 1) == 0)
  
          # next check that the successive differences between
          # the numbers are all 1, i.e., they are nuermicontiguous
          contiguous = np.all(np.diff(numbers) == 1)
  
      except AssertionError:
          raise ValueError("Input cannot be empty.")
  
      except TypeError:
          raise TypeError("Input should only contain numbers.")
  
      # we need both conditions to be true and we want to return
      # a regular Python `bool`, not a `numpy.bool_`
      return bool(ints_or_int_like_floats and contiguous)
  
imports: from __future__ import annotations
  import inspect
  import logging
  import sys
  import time
  from collections import Counter, defaultdict
  from csv import DictWriter, excel_tab
  from functools import wraps
  from importlib import import_module
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import joblib
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
  from sklearn.model_selection import KFold, LeaveOneGroupOut, ShuffleSplit, StratifiedKFold
  from skll.data import FeatureSet
  from skll.metrics import _CUSTOM_METRICS, _PREDEFINED_CUSTOM_METRICS, use_score_func
  from skll.types import ComputeEvalMetricsResults, ConfusionMatrix, FeaturesetIterator, FoldMapping, IdType, IndexIterator, LabelType, PathOrStr, SparseFeatureMatrix
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS, CORRELATION_METRICS, REGRESSION_ONLY_METRICS, UNWEIGHTED_KAPPA_METRICS, WEIGHTED_KAPPA_METRICS
  from skll.version import VERSION
label_desc: Use image node_id 4 for calling a global function with example usage: contiguous_ints_or_floats(numbers) and returns: bool