parameters: (x1, x2, out=None, **kwargs)
return_statement: return _mx_nd_np.logaddexp(x1, x2, out)
docstring: Logarithm of the sum of exponentiations of the inputs.
  
      Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where
      the calculated probabilities of events may be so small as to exceed the range of
      normal floating point numbers. In such cases the logarithm of the calculate
      probability is stored. This function allows adding probabilities stored
      in such a fashion.
  
      Parameters
      ----------
      x1 : ndarray or scalar
          Array of multipliers.
      x2 : ndarray or scalar, int
          Array of twos exponents.
      out : ndarray, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not, a freshly-allocated array is returned.
  
      Returns
      -------
      y : ndarray or scalar
          Logarithm of exp(x1) + exp(x2). This is a scalar if both x1 and x2 are scalars.
  
      Examples
      --------
      >>> prob1 = np.log(1e-50)
      >>> prob2 = np.log(2.5e-50)
      >>> prob12 = np.logaddexp(prob1, prob2)
      >>> prob12
      -113.87649168120691
      >>> np.exp(prob12)
      3.5000000000000057e-50
docstring_summary: Logarithm of the sum of exponentiations of the inputs.
docstring_tokens: Logarithm of the sum of exponentiations of the inputs .
function: def logaddexp(x1, x2, out=None, **kwargs):
      """
      Logarithm of the sum of exponentiations of the inputs.
  
      Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where
      the calculated probabilities of events may be so small as to exceed the range of
      normal floating point numbers. In such cases the logarithm of the calculate
      probability is stored. This function allows adding probabilities stored
      in such a fashion.
  
      Parameters
      ----------
      x1 : ndarray or scalar
          Array of multipliers.
      x2 : ndarray or scalar, int
          Array of twos exponents.
      out : ndarray, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not, a freshly-allocated array is returned.
  
      Returns
      -------
      y : ndarray or scalar
          Logarithm of exp(x1) + exp(x2). This is a scalar if both x1 and x2 are scalars.
  
      Examples
      --------
      >>> prob1 = np.log(1e-50)
      >>> prob2 = np.log(2.5e-50)
      >>> prob12 = np.logaddexp(prob1, prob2)
      >>> prob12
      -113.87649168120691
      >>> np.exp(prob12)
      3.5000000000000057e-50
      """
      return _mx_nd_np.logaddexp(x1, x2, out)
function_tokens: def logaddexp ( x1 , x2 , out = None , * * kwargs ) : return _mx_nd_np . logaddexp ( x1 , x2 , out )