code: def _npx_index_update(a, ind, val):
      """
      Update values to input according to given indexes.
      If multiple indices refer to the same location it is undefined which update is chosen; it may choose
      the order of updates arbitrarily and nondeterministically (e.g., due to concurrent updates on some
      hardware platforms). Recommend not to use repeate positions.
  
      Parameters
      ----------
      a : ndarray
          Input data. The array to be updated.
          Support dtype: 'float32', 'float64', 'int32', 'int64'.
      ind : ndarray
          Indexes for indicating update positions.
          For example, array([[0, 1], [2, 3], [4, 5]] indicates here are two positions to
          be updated, which is (0, 2, 4) and (1, 3, 5).
          Note: - 'ind' cannot be empty array '[]', for that case, please use operator 'add' instead.
                - 0 <= ind.ndim <= 2.
                - ind.dtype should be 'int32' or 'int64'
      val : ndarray
          Input data. The array to update the input 'a'.
          Support dtype: 'float32', 'float64', 'int32', 'int64'.
  
      Returns
      -------
      out : ndarray
          The output array.
  
      Examples
      --------
      >>> a = np.zeros((2, 3, 4))
      >>> ind = np.array([[0, 0], [0, 0], [0, 1]], dtype='int32')
      >>> val = np.arange(2).reshape(2) + 1
      >>> b = npx.index_update(a, ind, val)
      >>> b
      array([[[1., 2., 0., 0.],
              [0., 0., 0., 0.],
              [0., 0., 0., 0.]],
  
             [[0., 0., 0., 0.],
              [0., 0., 0., 0.],
              [0., 0., 0., 0.]]])
  
      >>> ind=np.array([[0, 0], [0, 1]], dtype='int32')
      >>> val = np.arange(8).reshape(2, 4)
      >>> b = npx.index_update(a, ind, val)
      >>> b
      array([[[0., 1., 2., 3.],
              [4., 5., 6., 7.],
              [0., 0., 0., 0.]],
  
             [[0., 0., 0., 0.],
              [0., 0., 0., 0.],
              [0., 0., 0., 0.]]])
  
      >>> val = np.arange(4).reshape(4)  # brocast 'val'
      >>> b = npx.index_update(a, ind, val)
      >>> b
      array([[[0., 1., 2., 3.],
              [0., 1., 2., 3.],
              [0., 0., 0., 0.]],
  
          [[0., 0., 0., 0.],
              [0., 0., 0., 0.],
              [0., 0., 0., 0.]]])
      """
      pass
  
file_path: mxnet/python/mxnet/_numpy_op_doc.py
label_desc: Use image node_id 15 for calling a global function with example usage: _npx_index_update(a, ind, val) without return types