





'''simple docstring'''






def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							float:



   """simple docstring"""


   __snake_case				:			Union[str, Any]       			=    float()
   try:
      __snake_case				:			Optional[int]       			=    mass * acceleration
   except Exception:
      return -0.0
   return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __UpperCamelCase										=  12.5
 __UpperCamelCase										=  10
 __UpperCamelCase										=  newtons_second_law_of_motion(mass, acceleration)
 print("The force is ", force, "N")









'''simple docstring'''






import torch
from torch import autograd




class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[Any]     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float     ,			__magic_name__				:      bool )				->						Optional[int]:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    inputs.numel()
					__snake_case				:			List[Any]       			=    int(0.005 * nb_elems ) + 1
					if sigmoid:
								__snake_case				:			Dict       			=    (torch.sigmoid(__magic_name__ ) > threshold).type(inputs.type() )
					else:
								__snake_case				:			Tuple       			=    (inputs > threshold).type(inputs.type() )
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__snake_case				:			Tuple       			=    inputs.flatten().kthvalue(max(nb_elems - nb_min     ,			1 ) ).values
								__snake_case				:			Union[str, Any]       			=    (inputs > k_threshold).type(inputs.type() )
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      int     ,			__magic_name__				:      Any )				->						Tuple:


					"""simple docstring"""

					return gradOutput, None, None





class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      Tuple     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						int:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Dict       			=    inputs.flatten().sort(descending=__magic_name__ )
					__snake_case				:			List[Any]       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Optional[Any]       			=    mask.flatten()
					__snake_case				:			List[str]       			=    0
					__snake_case				:			Any       			=    1
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[str]     ,			__magic_name__				:      Any )				->						Optional[Any]:


					"""simple docstring"""

					return gradOutput, None





class    _A   (				__lowercase		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Tuple       			=    inputs.abs().flatten().sort(descending=__magic_name__ )
					__snake_case				:			Any       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Union[str, Any]       			=    mask.flatten()
					__snake_case				:			Tuple       			=    0
					__snake_case				:			int       			=    1
					return mask


