





from datetime import datetime

import matplotlib.pyplot as plt
import torch

def   a_    ( lowerCAmelCase_      :			Tuple	):
   for param in module.parameters():
      __lowerCAmelCase								=	False

def   a_    ( ):
   __lowerCAmelCase								=	'cuda' if torch.cuda.is_available() else 'cpu'
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      __lowerCAmelCase								=	'mps'
   if device == "mps":
      print(
          'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
          ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
          ' with generations.'	)
   return device

def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	plt.imshow(UpperCamelCase_	)
   fig.axes.get_xaxis().set_visible(UpperCamelCase_	)
   fig.axes.get_yaxis().set_visible(UpperCamelCase_	)
   plt.show()




def   a_    ( ):
   __lowerCAmelCase								=	datetime.now()
   __lowerCAmelCase								=	current_time.strftime('%H:%M:%S'	)
   return timestamp



"""simple docstring"""





__magic_name__       =			"Tobias Carryer"

from time import time
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=int(time())):  # noqa: B008
				__SCREAMING_SNAKE_CASE            = multiplier
				__SCREAMING_SNAKE_CASE            = increment
				__SCREAMING_SNAKE_CASE            = modulo
				__SCREAMING_SNAKE_CASE            = seed




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (self.multiplier * self.seed + self.increment) % self.modulo
				return self.seed


if __name__ == "__main__":
							# Show the LCG in action.
							__magic_name__       =			LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
							while True:
														print(lcg.next_number())




