code: def dot(self, other):
          
          if type(other) == np.ndarray:
              if other.ndim > 1:
                  assert len(self) == other.shape[0], "dimension mismatch"
              return np.dot(self.array, other)
          elif _have_scipy and scipy.sparse.issparse(other):
              assert len(self) == other.shape[0], "dimension mismatch"
              return other.transpose().dot(self.toArray())
          else:
              assert len(self) == _vector_size(other), "dimension mismatch"
              if isinstance(other, SparseVector):
                  return other.dot(self)
              elif isinstance(other, Vector):
                  return np.dot(self.toArray(), other.toArray())
              else:
                  return np.dot(self.toArray(), other)
docs: Compute the dot product of two Vectors. We support
          (Numpy array, list, SparseVector, or SciPy sparse)
          and a target NumPy array that is either 1- or 2-dimensional.
          Equivalent to calling numpy.dot of the two vectors.
  
          >>> dense = DenseVector(array.array('d', [1., 2.]))
          >>> dense.dot(dense)
          5.0
          >>> dense.dot(SparseVector(2, [0, 1], [2., 1.]))
          4.0
          >>> dense.dot(range(1, 3))
          5.0
          >>> dense.dot(np.array(range(1, 3)))
          5.0
          >>> dense.dot([1.,])
          Traceback (most recent call last):
              ...
          AssertionError: dimension mismatch
          >>> dense.dot(np.reshape([1., 2., 3., 4.], (2, 2), order='F'))
          array([  5.,  11.])
          >>> dense.dot(np.reshape([1., 2., 3.], (3, 1), order='F'))
          Traceback (most recent call last):
              ...
          AssertionError: dimension mismatch