parameters: (p, m=1, k=None)
return_statement: return p
docstring: Returns an antiderivative (indefinite integral) of a polynomial.
  
      Note:
          Numpy object poly1d is currently not supported.
  
      Args:
          p (Union[int, float, bool, list, tuple, Tensor): Polynomial to integrate. A
              sequence is interpreted as polynomial coefficients.
          m (int, optional): Defaults to 1, Order of the antiderivative.
          k (Union[int, list of int]y, optinoal): Integration constants. They are given
              in the order of integration: those corresponding to highest-order terms
              come first. If None (default), all constants are assumed to be zero. If
              ``m = 1``, a single scalar can be given instead of a list.
  
      Returns:
          Tensor, a new polynomial representing the antiderivative.
  
      Raises:
          ValueError: If `p` has more than 1 dimensions.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.polyint([1, 1, 1]))
          [0.33333334 0.5        1.         0.        ]
docstring_summary: Returns an antiderivative (indefinite integral) of a polynomial.
docstring_tokens: Returns an antiderivative ( indefinite integral ) of a polynomial .
function: def polyint(p, m=1, k=None):
      """
      Returns an antiderivative (indefinite integral) of a polynomial.
  
      Note:
          Numpy object poly1d is currently not supported.
  
      Args:
          p (Union[int, float, bool, list, tuple, Tensor): Polynomial to integrate. A
              sequence is interpreted as polynomial coefficients.
          m (int, optional): Defaults to 1, Order of the antiderivative.
          k (Union[int, list of int]y, optinoal): Integration constants. They are given
              in the order of integration: those corresponding to highest-order terms
              come first. If None (default), all constants are assumed to be zero. If
              ``m = 1``, a single scalar can be given instead of a list.
  
      Returns:
          Tensor, a new polynomial representing the antiderivative.
  
      Raises:
          ValueError: If `p` has more than 1 dimensions.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import mindspore.numpy as np
          >>> print(np.polyint([1, 1, 1]))
          [0.33333334 0.5        1.         0.        ]
      """
      p = _to_poly1d(p)
      if m < 0:
          _raise_value_error('Order of derivative must be positive')
      if m == 0:
          return p
      if k is None:
          k = zeros(m, F.dtype(p))
      k = atleast_1d(_to_tensor(k))
      if k.size == 1:
          k = F.tile(k, (m,))
      k = F.expand_dims(k, -1)
      for i in range(m):
          coeff = _to_tensor(F.make_range(_type_convert(int, p.size), 0, -1))
          p = concatenate((true_divide(p, coeff), k[i]))
      return p
function_tokens: def polyint ( p , m = 1 , k = None ) : p = _to_poly1d ( p ) if m < 0 : _raise_value_error ( 'Order of derivative must be positive' ) if m == 0 : return p if k is None : k = zeros ( m , F . dtype ( p ) ) k = atleast_1d ( _to_tensor ( k ) ) if k . size == 1 : k = F . tile ( k , ( m , ) ) k = F . expand_dims ( k , - 1 ) for i in range ( m ) : coeff = _to_tensor ( F . make_range ( _type_convert ( int , p . size ) , 0 , - 1 ) ) p = concatenate ( ( true_divide ( p , coeff ) , k [ i ] ) ) return p