parameters: (x, ord=None, axis=None, keepdims=False)
return_statement: return res
docstring: Matrix or vector norm.
      This function is able to return one of eight different matrix norms, or one of an
      infinite number of vector norms (described below), depending on the value of the
      ord parameter.
  
      Note:
          Nuclear norm and 2-norm are not supported for matrices.
  
      Args:
          x (Union[int, float, bool, list, tuple, Tensor]): Input array. If `axis` is None,
              `x` must be 1-D or 2-D, unless `ord` is None. If both `axis` and `ord` are None,
              the 2-norm of ``x.ravel`` will be returned.
          ord (Union[None, 'fro', 'nuc', inf, -inf, int, float], optional): Order of the norm.
              inf means numpy’s inf object. The default is None.
          axis (Union[None, int, 2-tuple of integers], optional): If `axis` is an integer, it
              specifies the axis of `x` along which to compute the vector norms. If `axis` is
              a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of
              these matrices are computed. If `axis` is None then either a vector norm (when x
              is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default is None.
          keepdims (boolean, optional): If this is set to True, the axes which are normed over
              are left in the result as dimensions with size one. With this option the result
              will broadcast correctly against the original `x`.
  
      Returns:
          Tensor, norm of the matrix or vector(s).
  
      Raises:
          ValueError: If the norm order is not defined.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.norm(np.arange(9).astype(np.float32)))
          14.282857
docstring_summary: Matrix or vector norm.
      This function is able to return one of eight different matrix norms, or one of an
      infinite number of vector norms (described below), depending on the value of the
      ord parameter.
docstring_tokens: Matrix or vector norm . This function is able to return one of eight different matrix norms or one of an infinite number of vector norms ( described below ) depending on the value of the ord parameter .
function: def norm(x, ord=None, axis=None, keepdims=False): # pylint: disable=redefined-builtin
      """
      Matrix or vector norm.
      This function is able to return one of eight different matrix norms, or one of an
      infinite number of vector norms (described below), depending on the value of the
      ord parameter.
  
      Note:
          Nuclear norm and 2-norm are not supported for matrices.
  
      Args:
          x (Union[int, float, bool, list, tuple, Tensor]): Input array. If `axis` is None,
              `x` must be 1-D or 2-D, unless `ord` is None. If both `axis` and `ord` are None,
              the 2-norm of ``x.ravel`` will be returned.
          ord (Union[None, 'fro', 'nuc', inf, -inf, int, float], optional): Order of the norm.
              inf means numpy’s inf object. The default is None.
          axis (Union[None, int, 2-tuple of integers], optional): If `axis` is an integer, it
              specifies the axis of `x` along which to compute the vector norms. If `axis` is
              a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of
              these matrices are computed. If `axis` is None then either a vector norm (when x
              is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default is None.
          keepdims (boolean, optional): If this is set to True, the axes which are normed over
              are left in the result as dimensions with size one. With this option the result
              will broadcast correctly against the original `x`.
  
      Returns:
          Tensor, norm of the matrix or vector(s).
  
      Raises:
          ValueError: If the norm order is not defined.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.norm(np.arange(9).astype(np.float32)))
          14.282857
      """
      if not isinstance(ord, (int, float)) and not _in(ord, (None, 'fro', 'nuc', inf, -inf)):
          _raise_value_error('invalid value for `ord`')
      x = _to_tensor(x)
      ndim = F.rank(x)
      if axis is None:
          if ord is None:
              x = x.ravel()
          if F.rank(x) not in (1, 2):
              _raise_value_error('for None axis, array must a vector or a 2-D matrix')
          axis = F.make_range(F.rank(x))
      axis = _check_axis_valid(axis, F.rank(x))
  
      if len(axis) == 1:
          res = _vector_norm(x, ord, axis, keepdims)
      elif len(axis) == 2:
          res = _matrix_norm(x, ord, axis, keepdims)
      else:
          return _raise_value_error('invalid number of dimensions to norm')
  
      if keepdims and ndim > F.rank(res):
          res = _expand(res, ndim)
      return res
function_tokens: def norm ( x , ord = None , axis = None , keepdims = False ) : # pylint: disable=redefined-builtin if not isinstance ( ord , ( int , float ) ) and not _in ( ord , ( None , 'fro' , 'nuc' , inf , - inf ) ) : _raise_value_error ( 'invalid value for `ord`' ) x = _to_tensor ( x ) ndim = F . rank ( x ) if axis is None : if ord is None : x = x . ravel ( ) if F . rank ( x ) not in ( 1 , 2 ) : _raise_value_error ( 'for None axis, array must a vector or a 2-D matrix' ) axis = F . make_range ( F . rank ( x ) ) axis = _check_axis_valid ( axis , F . rank ( x ) ) if len ( axis ) == 1 : res = _vector_norm ( x , ord , axis , keepdims ) elif len ( axis ) == 2 : res = _matrix_norm ( x , ord , axis , keepdims ) else : return _raise_value_error ( 'invalid number of dimensions to norm' ) if keepdims and ndim > F . rank ( res ) : res = _expand ( res , ndim ) return res