"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	List[str]     )	->				int:
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  _lowerCAmelCase						:	Tuple        =					(boundary[1] - boundary[0]) / steps
  _lowerCAmelCase						:	Union[str, Any]        =					boundary[0]
  _lowerCAmelCase						:	Optional[int]        =					boundary[1]
  _lowerCAmelCase						:	Optional[int]        =					make_points(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					0.0
  y += (h / 2.0) * f(_lowerCamelCase     )
  for i in x_i:
    # print(i)
    y += h * f(_lowerCamelCase     )
  y += (h / 2.0) * f(_lowerCamelCase     )
  return y





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[str]     )	->				str:
  _lowerCAmelCase						:	Tuple        =					a + h
  while x < (b - h):
    yield x
    _lowerCAmelCase						:	Optional[Any]        =					x + h





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				Optional[int]:  # enter your function here
  _lowerCAmelCase						:	str        =					(x - 0) * (x - 0)
  return y





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


if __name__ == "__main__":
     main()





"""simple docstring"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




