

import torch

from diffusers import StableDiffusionPipeline


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

lowercase_             =     'A photo of sks dog in a bucket'
lowercase_             =     pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

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





from math import pow, sqrt
def   a   (    *A__			:     float  )					->   bool:





      """simple docstring"""



      _lowercase			 =len(A__  ) > 0 and all(value > 0.0 for value in values  )
      return result
def   a   (    A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__  )
          else ValueError('Input Error: Molar mass values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,     2  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )







def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,     2  ) / molar_mass   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )


