parameters: (n, k=0, m=None, device=None)
return_statement: return nonzero(~tri(N=n, M=m, k=k-1, dtype=bool, device=device))
docstring: r"""
      Return the indices for the upper-triangle of an (n, m) array.
  
      Parameters
      ----------
      n : int
          The size of the arrays for which the returned indices will
          be valid.
      k : int, optional
          Diagonal offset (see `triu` for details).
      m : int, optional
          .. versionadded:: 1.9.0
  
          The column dimension of the arrays for which the returned
          arrays will be valid.
          By default `m` is taken equal to `n`.
  
  
      Returns
      -------
      inds : tuple, shape(2) of ndarrays, shape(`n`)
          The indices for the triangle. The returned tuple contains two arrays,
          each with the indices along one dimension of the array.  Can be used
          to slice a ndarray of shape(`n`, `n`).
  
      See also
      --------
      tril_indices : similar function, for lower-triangular.
      mask_indices : generic function accepting an arbitrary mask function.
      triu, tril
  
      Examples
      --------
      Compute two different sets of indices to access 4x4 arrays, one for the
      upper triangular part starting at the main diagonal, and one starting two
      diagonals further right:
  
      >>> iu1 = np.triu_indices(4)
      >>> iu2 = np.triu_indices(4, 2)
  
      Here is how they can be used with a sample array:
  
      >>> a = np.arange(16).reshape(4, 4)
      >>> a
      array([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]])
  
      Both for indexing:
  
      >>> a[iu1]
      array([ 0,  1,  2, ..., 10, 11, 15])
  
      And for assigning values:
  
      >>> a[iu1] = -1
      >>> a
      array([[-1, -1, -1, -1],
             [ 4, -1, -1, -1],
             [ 8,  9, -1, -1],
             [12, 13, 14, -1]])
  
      These cover only a small part of the whole array (two diagonals right
      of the main one):
  
      >>> a[iu2] = -10
      >>> a
      array([[ -1,  -1, -10, -10],
             [  4,  -1,  -1, -10],
             [  8,   9,  -1,  -1],
             [ 12,  13,  14,  -1]])
docstring_summary: r"""
      Return the indices for the upper-triangle of an (n, m) array.
docstring_tokens: r Return the indices for the upper - triangle of an ( n m ) array .
function: def triu_indices(n, k=0, m=None, device=None):
      r"""
      Return the indices for the upper-triangle of an (n, m) array.
  
      Parameters
      ----------
      n : int
          The size of the arrays for which the returned indices will
          be valid.
      k : int, optional
          Diagonal offset (see `triu` for details).
      m : int, optional
          .. versionadded:: 1.9.0
  
          The column dimension of the arrays for which the returned
          arrays will be valid.
          By default `m` is taken equal to `n`.
  
  
      Returns
      -------
      inds : tuple, shape(2) of ndarrays, shape(`n`)
          The indices for the triangle. The returned tuple contains two arrays,
          each with the indices along one dimension of the array.  Can be used
          to slice a ndarray of shape(`n`, `n`).
  
      See also
      --------
      tril_indices : similar function, for lower-triangular.
      mask_indices : generic function accepting an arbitrary mask function.
      triu, tril
  
      Examples
      --------
      Compute two different sets of indices to access 4x4 arrays, one for the
      upper triangular part starting at the main diagonal, and one starting two
      diagonals further right:
  
      >>> iu1 = np.triu_indices(4)
      >>> iu2 = np.triu_indices(4, 2)
  
      Here is how they can be used with a sample array:
  
      >>> a = np.arange(16).reshape(4, 4)
      >>> a
      array([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]])
  
      Both for indexing:
  
      >>> a[iu1]
      array([ 0,  1,  2, ..., 10, 11, 15])
  
      And for assigning values:
  
      >>> a[iu1] = -1
      >>> a
      array([[-1, -1, -1, -1],
             [ 4, -1, -1, -1],
             [ 8,  9, -1, -1],
             [12, 13, 14, -1]])
  
      These cover only a small part of the whole array (two diagonals right
      of the main one):
  
      >>> a[iu2] = -10
      >>> a
      array([[ -1,  -1, -10, -10],
             [  4,  -1,  -1, -10],
             [  8,   9,  -1,  -1],
             [ 12,  13,  14,  -1]])
          """
      return nonzero(~tri(N=n, M=m, k=k-1, dtype=bool, device=device))
function_tokens: def triu_indices ( n , k = 0 , m = None , device = None ) : return nonzero ( ~ tri ( N = n , M = m , k = k - 1 , dtype = bool , device = device ) )