def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					return "\n".join(
					    f'''{number} * {i} = {number * i}''' for i in range(1				,    number_of_terms + 1						)						)


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=10))





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       '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 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



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




