
"""simple docstring"""

from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCamelCase      :							List[str]          =					6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2






def 				A		(							snake_case :float							,   snake_case :float							,   snake_case :float							,   snake_case :float						) ->      dict[str, float]:
 __UpperCamelCase							    =			mass_a * mass_a

 if (force, mass_a, mass_a, distance).count(0						) != 1:
  raise ValueError('One and only one argument must be 0'						)
 if force < 0:
  raise ValueError('Gravitational force can not be negative'						)
 if distance < 0:
  raise ValueError('Distance can not be negative'						)
 if mass_a < 0 or mass_a < 0:
  raise ValueError('Mass can not be negative'						)
 if force == 0:
  __UpperCamelCase							    =			GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
  return {"force": force}
 elif mass_a == 0:
  __UpperCamelCase							    =			(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
  return {"mass_1": mass_a}
 elif mass_a == 0:
  __UpperCamelCase							    =			(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
  return {"mass_2": mass_a}
 elif distance == 0:
  __UpperCamelCase							    =			(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
  return {"distance": distance}
 raise ValueError('One and only one argument must be 0'						)


# Run doctest
if __name__ == "__main__":
      import doctest

      doctest.testmod()





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



