





"""simple docstring"""






import math
import random

def      __A   (      a_    :float ,							a_    :bool = False)		->		float:
				if deriv:
								return value * (1 - value)
				return 1 / (1 + math.exp(-value))


# Initial Value
A			      =	0.02

def      __A   (      a_    :int ,							a_    :int)		->		float:
				__a			:    List[Any]      							=	float(2 * (random.randint(1 ,							1_00)) - 1)

				for _ in range(a_):
								# Forward propagation
								__a			:    int      							=	sigmoid_function(INITIAL_VALUE * weight)
								# How much did we miss?
								__a			:    int      							=	(expected / 1_00) - layer_a
								# Error delta
								__a			:    Dict      							=	layer_1_error * sigmoid_function(a_ ,							a_)
								# Update weight
								weight += INITIAL_VALUE * layer_1_delta

				return layer_a * 1_00


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A			      =	int(input('''Expected value: '''))
		A			      =	int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))

"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

