code: def dsplit(a, indices_or_sections):
      """
      Split tensor into multiple sub-tensors along the 3rd axis (depth).
  
      Please refer to the `split` documentation.  `dsplit` is equivalent
      to `split` with ``axis=2``, the array is always split along the third
      axis provided the tensor dimension is greater than or equal to 3.
  
      See Also
      --------
      split : Split a tensor into multiple sub-arrays of equal size.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> x = mt.arange(16.0).reshape(2, 2, 4)
      >>> x.execute()
      array([[[  0.,   1.,   2.,   3.],
              [  4.,   5.,   6.,   7.]],
             [[  8.,   9.,  10.,  11.],
              [ 12.,  13.,  14.,  15.]]])
      >>> mt.dsplit(x, 2).execute()
      [array([[[  0.,   1.],
              [  4.,   5.]],
             [[  8.,   9.],
              [ 12.,  13.]]]),
       array([[[  2.,   3.],
              [  6.,   7.]],
             [[ 10.,  11.],
              [ 14.,  15.]]])]
      >>> mt.dsplit(x, mt.array([3, 6])).execute()
      [array([[[  0.,   1.,   2.],
              [  4.,   5.,   6.]],
             [[  8.,   9.,  10.],
              [ 12.,  13.,  14.]]]),
       array([[[  3.],
              [  7.]],
             [[ 11.],
              [ 15.]]]),
       array([], dtype=float64)]
  
      """
      ary = a
      a = astensor(a)
  
      if a.ndim < 3:
          raise ValueError(
              "dsplit only works on tensors of 3 or more dimensions"
          )
      return split(ary, indices_or_sections, 2)
  
label_desc: Use image node_id 1 for calling a global function with example usage: dsplit(a, indices_or_sections) and returns: split