code: 
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			(boundary[1] - boundary[0]) / steps
         _lowercase					:				str           =			boundary[0]
         _lowercase					:				List[Any]           =			boundary[1]
         _lowercase					:				str           =			make_points(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
         _lowercase					:				Optional[Any]           =			0.0
         y += (h / 3.0) * f(UpperCAmelCase_			)
         _lowercase					:				Optional[int]           =			2
         for i in x_i:
                y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase_			)
                cnt += 1
         y += (h / 3.0) * f(UpperCAmelCase_			)
         return y
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Union[str, Any]           =			a + h
         while x < (b - h):
                yield x
                _lowercase					:				Optional[Any]           =			x + h
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:  # enter your function here
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Tuple           =			(x - 0) * (x - 0)
         return y
  
  def   UpperCamelCase__   (    )						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			0.0  # Lower bound of integration
         _lowercase					:				int           =			1.0  # Upper bound of integration
         _lowercase					:				int           =			10.0  # define number of steps or resolution
         _lowercase					:				Optional[Any]           =			[a, b]  # define boundary of integration
         _lowercase					:				Optional[int]           =			method_a(UpperCAmelCase_    ,     UpperCAmelCase_			)
         print(F'y = {y}'			)
  
  
  if __name__ == "__main__":
     main()
style_context: 
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
              _lowercase					:				Dict           =			image.to(self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample
  
                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,), "This is a local test"
  
              return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"