






'''simple docstring'''

def 							snake_case					(     a_							: int = 10    ,   a_							: int = 22	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							range(1    ,   a_	)
      UpperCamelCase_				:   str					    =							range(1    ,   a_	)
      return sum(
          1 for power in powers for base in bases if len(str(base**power	)	) == power	)


if __name__ == "__main__":
      print(f'{solution(10, 22) = }')














'''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")






