parameters: (a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False)
return_statement: return bool(res)
docstring: Returns True if two arrays are element-wise equal within a tolerance.
  
      The tolerance values are positive, typically very small numbers.  The
      relative difference (`rtol` * abs(`b`)) and the absolute difference
      `atol` are added together to compare against the absolute difference
      between `a` and `b`.
  
      NaNs are treated as equal if they are in the same place and if
      ``equal_nan=True``.  Infs are treated as equal if they are in the same
      place and of the same sign in both arrays.
  
      Parameters
      ----------
      a, b : array_like
          Input arrays to compare.
      rtol : float
          The relative tolerance parameter (see Notes).
      atol : float
          The absolute tolerance parameter (see Notes).
      equal_nan : bool
          Whether to compare NaN's as equal.  If True, NaN's in `a` will be
          considered equal to NaN's in `b` in the output array.
  
          .. versionadded:: 1.10.0
  
      Returns
      -------
      allclose : bool
          Returns True if the two arrays are equal within the given
          tolerance; False otherwise.
  
      See Also
      --------
      isclose, all, any, equal
  
      Notes
      -----
      If the following equation is element-wise True, then allclose returns
      True.
  
       absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
  
      The above equation is not symmetric in `a` and `b`, so that
      ``allclose(a, b)`` might be different from ``allclose(b, a)`` in
      some rare cases.
  
      The comparison of `a` and `b` uses standard broadcasting, which
      means that `a` and `b` need not have the same shape in order for
      ``allclose(a, b)`` to evaluate to True.  The same is true for
      `equal` but not `array_equal`.
  
      Examples
      --------
      >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
      False
      >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
      True
      >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
      False
      >>> np.allclose([1.0, np.nan], [1.0, np.nan])
      False
      >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
      True
docstring_summary: Returns True if two arrays are element-wise equal within a tolerance.
docstring_tokens: Returns True if two arrays are element - wise equal within a tolerance .
function: def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
      """
      Returns True if two arrays are element-wise equal within a tolerance.
  
      The tolerance values are positive, typically very small numbers.  The
      relative difference (`rtol` * abs(`b`)) and the absolute difference
      `atol` are added together to compare against the absolute difference
      between `a` and `b`.
  
      NaNs are treated as equal if they are in the same place and if
      ``equal_nan=True``.  Infs are treated as equal if they are in the same
      place and of the same sign in both arrays.
  
      Parameters
      ----------
      a, b : array_like
          Input arrays to compare.
      rtol : float
          The relative tolerance parameter (see Notes).
      atol : float
          The absolute tolerance parameter (see Notes).
      equal_nan : bool
          Whether to compare NaN's as equal.  If True, NaN's in `a` will be
          considered equal to NaN's in `b` in the output array.
  
          .. versionadded:: 1.10.0
  
      Returns
      -------
      allclose : bool
          Returns True if the two arrays are equal within the given
          tolerance; False otherwise.
  
      See Also
      --------
      isclose, all, any, equal
  
      Notes
      -----
      If the following equation is element-wise True, then allclose returns
      True.
  
       absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
  
      The above equation is not symmetric in `a` and `b`, so that
      ``allclose(a, b)`` might be different from ``allclose(b, a)`` in
      some rare cases.
  
      The comparison of `a` and `b` uses standard broadcasting, which
      means that `a` and `b` need not have the same shape in order for
      ``allclose(a, b)`` to evaluate to True.  The same is true for
      `equal` but not `array_equal`.
  
      Examples
      --------
      >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
      False
      >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
      True
      >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
      False
      >>> np.allclose([1.0, np.nan], [1.0, np.nan])
      False
      >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
      True
  
      """
      res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
      return bool(res)
function_tokens: def allclose ( a , b , rtol = 1.e-5 , atol = 1.e-8 , equal_nan = False ) : res = all ( isclose ( a , b , rtol = rtol , atol = atol , equal_nan = equal_nan ) ) return bool ( res )