






def   __A       (  _lowercase       ,      _lowercase			):

					'''simple docstring'''







					_A										=			(boundary[1] - boundary[0]) / steps
					_A										=			boundary[0]
					_A										=			boundary[1]
					_A										=			make_points(__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase			)
					_A										=			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   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







					_A										=			a + h
					while x < (b - h):
										yield x
										_A										=			x + h




def   __A       (  _lowercase			):  # 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(__lowerCAmelCase       ,      __lowerCAmelCase			)
					print(f"""y = {y}"""			)


if __name__ == "__main__":
				main()








from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp