






'''simple docstring'''

from __future__ import annotations



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: Union[str, Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							sorted(numsa + numsa	)
      UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							divmod(len(SCREAMING_SNAKE_CASE_	)    ,   2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase							=[float(x) for x in input("Enter the elements of first array: ").split()]
      UpperCamelCase							=[float(x) for x in input("Enter the elements of second array: ").split()]
      print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')














'''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")






