code: 
  
  
  """simple docstring"""
  
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				(boundary[1] - boundary[0]) / steps
         _a          =				boundary[0]
         _a          =				boundary[1]
         _a          =				make_points(a,		a,		a					)
         _a          =				0.0
         y += (h / 3.0) * f(a					)
         _a          =				2
         for i in x_i:
                y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a					)
                cnt += 1
         y += (h / 3.0) * f(a					)
         return y
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				a + h
         while x < (b - h):
                yield x
                _a          =				x + h
  
  
  
  def 						__a      (  a					):  # enter your function here
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				(x - 0) * (x - 0)
         return y
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				0.0  # Lower bound of integration
         _a          =				1.0  # Upper bound of integration
         _a          =				10.0  # define number of steps or resolution
         _a          =				[a, b]  # define boundary of integration
         _a          =				method_a(a,		a					)
         print(F'y = {y}'					)
  
  
  if __name__ == "__main__":
     main()
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
     lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'
  
  
  
  
     def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
            super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
            _a          =				self.feature_extractor
            _a          =				False
  
  
  
  
     def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*UpperCamelCase__				,  **UpperCamelCase__       )
  
            if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
                   _a          =				kwargs.pop("raw_speech"       )
            else:
                   _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
            if len(UpperCamelCase__       ) > 0:
                   _a          =				args[0]
                   _a          =				args[1:]
  
            if audio is None and text is None:
                   raise ValueError("You need to specify either an `audio` or `text` input to process."       )
  
            if audio is not None:
                   _a          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
            if text is not None:
                   _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )
  
            if text is None:
                   return inputs
            elif audio is None:
                   return encodings
            else:
                   _a          =				encodings["input_ids"]
                   return inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @contextmanager
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            warnings.warn(
                "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                "your audio inputs, or in a separate call."       )
            _a          =				True
            _a          =				self.tokenizer
            yield
            _a          =				self.feature_extractor
            _a          =				False
  