code: def clip_and_round(
      x: np.ndarray,
      clip_values: Optional["CLIP_VALUES_TYPE"],
      round_samples: float,
  ) -> np.ndarray:
      """
      Rounds the input to the correct level of granularity.
      Useful to ensure data passed to classifier can be represented
      in the correct domain, e.g., [0, 255] integers verses [0,1]
      or [0, 255] floating points.
  
      :param x: Sample input with shape as expected by the model.
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
             for features, or `None` if no clipping should be performed.
      :param round_samples: The resolution of the input domain to round the data to, e.g., 1.0, or 1/255. Set to 0 to
             disable.
      """
      if round_samples == 0.0:
          return x
      if clip_values is not None:
          np.clip(x, clip_values[0], clip_values[1], out=x)
      x = np.around(x / round_samples) * round_samples
      return x
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import math
  import os
  import shutil
  import sys
  import tarfile
  import warnings
  import zipfile
  from functools import wraps
  from inspect import signature
  from typing import TYPE_CHECKING, Callable, List, Dict, Optional, Tuple, Union
  import numpy
  import six
  from scipy.special import gammainc
  from tqdm.auto import tqdm
  from art import config
label_desc: Use image node_id 38 for calling a global function with example usage: clip_and_round(x, clip_values, round_samples) and returns: x, x