parameters: (a, b)
return_statement: return a.ravel()[:, newaxis]*b.ravel()[newaxis,:]
docstring: Compute the outer product of two vectors.
  
      Given two vectors, ``a = [a0, a1, ..., aM]`` and
      ``b = [b0, b1, ..., bN]``,
      the outer product [1]_ is::
  
        [[a0*b0  a0*b1 ... a0*bN ]
         [a1*b0    .
         [ ...          .
         [aM*b0            aM*bN ]]
  
      Parameters
      ----------
      a : (M,) array_like
          First input vector.  Input is flattened if
          not already 1-dimensional.
      b : (N,) array_like
          Second input vector.  Input is flattened if
          not already 1-dimensional.
  
      Returns
      -------
      out : (M, N) ndarray
          ``out[i, j] = a[i] * b[j]``
  
      See also
      --------
      inner, einsum
  
      References
      ----------
      .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd
               ed., Baltimore, MD, Johns Hopkins University Press, 1996,
               pg. 8.
  
      Examples
      --------
      Make a (*very* coarse) grid for computing a Mandelbrot set:
  
      >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
      >>> rl
      array([[-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.]])
      >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
      >>> im
      array([[ 0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j],
             [ 0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j],
             [ 0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
             [ 0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j],
             [ 0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j]])
      >>> grid = rl + im
      >>> grid
      array([[-2.+2.j, -1.+2.j,  0.+2.j,  1.+2.j,  2.+2.j],
             [-2.+1.j, -1.+1.j,  0.+1.j,  1.+1.j,  2.+1.j],
             [-2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j],
             [-2.-1.j, -1.-1.j,  0.-1.j,  1.-1.j,  2.-1.j],
             [-2.-2.j, -1.-2.j,  0.-2.j,  1.-2.j,  2.-2.j]])
  
      An example using a "vector" of letters:
  
      >>> x = np.array(['a', 'b', 'c'], dtype=object)
      >>> np.outer(x, [1, 2, 3])
      array([[a, aa, aaa],
             [b, bb, bbb],
             [c, cc, ccc]], dtype=object)
docstring_summary: Compute the outer product of two vectors.
docstring_tokens: Compute the outer product of two vectors .
function: def outer(a, b):
      """
      Compute the outer product of two vectors.
  
      Given two vectors, ``a = [a0, a1, ..., aM]`` and
      ``b = [b0, b1, ..., bN]``,
      the outer product [1]_ is::
  
        [[a0*b0  a0*b1 ... a0*bN ]
         [a1*b0    .
         [ ...          .
         [aM*b0            aM*bN ]]
  
      Parameters
      ----------
      a : (M,) array_like
          First input vector.  Input is flattened if
          not already 1-dimensional.
      b : (N,) array_like
          Second input vector.  Input is flattened if
          not already 1-dimensional.
  
      Returns
      -------
      out : (M, N) ndarray
          ``out[i, j] = a[i] * b[j]``
  
      See also
      --------
      inner, einsum
  
      References
      ----------
      .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd
               ed., Baltimore, MD, Johns Hopkins University Press, 1996,
               pg. 8.
  
      Examples
      --------
      Make a (*very* coarse) grid for computing a Mandelbrot set:
  
      >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
      >>> rl
      array([[-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.],
             [-2., -1.,  0.,  1.,  2.]])
      >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
      >>> im
      array([[ 0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j],
             [ 0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j],
             [ 0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
             [ 0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j],
             [ 0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j]])
      >>> grid = rl + im
      >>> grid
      array([[-2.+2.j, -1.+2.j,  0.+2.j,  1.+2.j,  2.+2.j],
             [-2.+1.j, -1.+1.j,  0.+1.j,  1.+1.j,  2.+1.j],
             [-2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j],
             [-2.-1.j, -1.-1.j,  0.-1.j,  1.-1.j,  2.-1.j],
             [-2.-2.j, -1.-2.j,  0.-2.j,  1.-2.j,  2.-2.j]])
  
      An example using a "vector" of letters:
  
      >>> x = np.array(['a', 'b', 'c'], dtype=object)
      >>> np.outer(x, [1, 2, 3])
      array([[a, aa, aaa],
             [b, bb, bbb],
             [c, cc, ccc]], dtype=object)
  
      """
      a = asarray(a)
      b = asarray(b)
      return a.ravel()[:, newaxis]*b.ravel()[newaxis,:]
function_tokens: def outer ( a , b ) : a = asarray ( a ) b = asarray ( b ) return a . ravel ( ) [ : , newaxis ] * b . ravel ( ) [ newaxis , : ]