def vector_norm(input, porder=None, axis=None, keepdim=False, asvector=False, name=None):
    '\n        Calculate the p-order vector norm for certain  dimension of Tensor `input`.\n        Args:\n          input (Variable): Tensor, data type float32, float64.\n          porder (float, optional): None for porder=2.0.\n          axis (int, optional): None for last dimension.\n          keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n        '
    if paddle.in_dynamic_mode():
        if (axis is None):
            axis = (- 1)
        return _C_ops.p_norm(input, 'porder', porder, 'axis', axis, 'keepdim', keepdim, 'asvector', asvector)
    if (porder is not None):
        check_type(porder, 'porder', (float, int), 'p_norm')
    if (axis is not None):
        check_type(axis, 'axis', int, 'p_norm')
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'p_norm')
    attrs = {'axis': (axis if (axis is not None) else (- 1)), 'porder': (float(porder) if (porder is not None) else 2.0), 'keepdim': keepdim, 'asvector': asvector, 'epsilon': 1e-12}
    helper = LayerHelper('p_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='p_norm', inputs={'X': input}, outputs={'Out': out}, attrs=attrs)
    return out
Calculate the p-order vector norm for certain  dimension of Tensor `input`.
Args:
  input (Variable): Tensor, data type float32, float64.
  porder (float, optional): None for porder=2.0.
  axis (int, optional): None for last dimension.
  keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
def vector_norm(input, porder=None, axis=None, keepdim=False, asvector=False, name=None):
    '\n        Calculate the p-order vector norm for certain  dimension of Tensor `input`.\n        Args:\n          input (Variable): Tensor, data type float32, float64.\n          porder (float, optional): None for porder=2.0.\n          axis (int, optional): None for last dimension.\n          keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n        '
    if paddle.in_dynamic_mode():
        if (axis is None):
            axis = (- 1)
        return _C_ops.p_norm(input, 'porder', porder, 'axis', axis, 'keepdim', keepdim, 'asvector', asvector)
    if (porder is not None):
        check_type(porder, 'porder', (float, int), 'p_norm')
    if (axis is not None):
        check_type(axis, 'axis', int, 'p_norm')
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'p_norm')
    attrs = {'axis': (axis if (axis is not None) else (- 1)), 'porder': (float(porder) if (porder is not None) else 2.0), 'keepdim': keepdim, 'asvector': asvector, 'epsilon': 1e-12}
    helper = LayerHelper('p_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='p_norm', inputs={'X': input}, outputs={'Out': out}, attrs=attrs)
    return out