
"""simple docstring"""




def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Union[str, Any]  )			->    Optional[int]:
       # "Simpson Rule"
       # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
       lowercase   :    Any			=							(boundary[1] - boundary[0]) / steps
       lowercase   :    List[str]			=							boundary[0]
       lowercase   :    Any			=							boundary[1]
       lowercase   :    List[Any]			=							make_points(__snake_case					,			__snake_case					,			__snake_case  )
       lowercase   :    Union[str, Any]			=							0.0
       y += (h / 3.0) * f(__snake_case  )
       lowercase   :    Union[str, Any]			=							2
       for i in x_i:
              y += (h / 3) * (4 - 2 * (cnt % 2)) * f(__snake_case  )
              cnt += 1
       y += (h / 3.0) * f(__snake_case  )
       return y


def   __magic_name__     (				__snake_case    :	int					,			__snake_case    :	str					,			__snake_case    :	Union[str, Any]  )			->    Any:
       lowercase   :    Tuple			=							a + h
       while x < (b - h):
              yield x
              lowercase   :    Union[str, Any]			=							x + h


def   __magic_name__     (				__snake_case    :	str  )			->    int:  # enter your function here
       lowercase   :    List[Any]			=							(x - 0) * (x - 0)
       return y


def   __magic_name__     (				)			->    Tuple:
       lowercase   :    List[str]			=							0.0  # Lower bound of integration
       lowercase   :    Optional[Any]			=							1.0  # Upper bound of integration
       lowercase   :    List[Any]			=							10.0  # define number of steps or resolution
       lowercase   :    Dict			=							[a, b]  # define boundary of integration
       lowercase   :    Optional[Any]			=							method_a(__snake_case					,			__snake_case  )
       print(f"""y = {y}"""  )


if __name__ == "__main__":
   main()




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


