






def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							float()
		try:
				__A    =							mass * acceleration
		except Exception:
				return -0.0
		return force


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# demo
					snake_case_   :	List[Any]							      =12.5
					snake_case_   :	List[Any]							      =10
					snake_case_   :	List[Any]							      =newtons_second_law_of_motion(mass, acceleration)
					print('''The force is ''', force, '''N''')




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




