






'''simple docstring'''





def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						a_				     =   (boundary[1] - boundary[0]) / steps
						a_				     =   boundary[0]
						a_				     =   boundary[1]
						a_				     =   make_points(UpperCAmelCase_	,UpperCAmelCase_	,UpperCAmelCase_				)
						a_				     =   0.0
						y += (h / 3.0) * f(UpperCAmelCase_				)
						a_				     =   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				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


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



def 			__UpperCAmelCase				(lowercase__				)		->				List[Any]:  # enter your function here

						'''simple docstring'''


						a_				     =   (x - 0) * (x - 0)
						return y



def 			__UpperCAmelCase				()		->				str:

						'''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(UpperCAmelCase_	,UpperCAmelCase_				)
						print(F"""y = {y}"""				)


if __name__ == "__main__":
							main()











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



