def frobenius_norm(input, dim=None, keepdim=False, name=None):
    '\n        The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n        Args:\n          input (Variable): Tensor, data type float32, float64.\n          dim (list, optional): None for last two dimensions.\n          keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n        '
    if ((dim is not None) and (not (isinstance(dim, list) and (len(dim) == 2)))):
        raise ValueError('The dim of frobenius norm op should be None or two elements list!')
    if paddle.in_dynamic_mode():
        if (dim is None):
            return _C_ops.frobenius_norm(input, 'keep_dim', keepdim, 'reduce_all', True)
        return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim, 'reduce_all', False)
    attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}
    if (dim is None):
        attrs['reduce_all'] = True
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'frobenius_norm')
    helper = LayerHelper('frobenius_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='frobenius_norm', inputs={'X': input}, outputs={'Out': out}, attrs=attrs)
    return out
The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args:
  input (Variable): Tensor, data type float32, float64.
  dim (list, optional): None for last two dimensions.
  keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
def frobenius_norm(input, dim=None, keepdim=False, name=None):
    '\n        The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n        Args:\n          input (Variable): Tensor, data type float32, float64.\n          dim (list, optional): None for last two dimensions.\n          keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n        '
    if ((dim is not None) and (not (isinstance(dim, list) and (len(dim) == 2)))):
        raise ValueError('The dim of frobenius norm op should be None or two elements list!')
    if paddle.in_dynamic_mode():
        if (dim is None):
            return _C_ops.frobenius_norm(input, 'keep_dim', keepdim, 'reduce_all', True)
        return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim, 'reduce_all', False)
    attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}
    if (dim is None):
        attrs['reduce_all'] = True
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'frobenius_norm')
    helper = LayerHelper('frobenius_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='frobenius_norm', inputs={'X': input}, outputs={'Out': out}, attrs=attrs)
    return out