code: def dstack(tup):
      """
      Stack tensors in sequence depth wise (along third axis).
  
      This is equivalent to concatenation along the third axis after 2-D tensors
      of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
      `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
      `dsplit`.
  
      This function makes most sense for arrays with up to 3 dimensions. For
      instance, for pixel-data with a height (first axis), width (second axis),
      and r/g/b channels (third axis). The functions `concatenate`, `stack` and
      `block` provide more general stacking and concatenation operations.
  
      Parameters
      ----------
      tup : sequence of tensors
          The tensors must have the same shape along all but the third axis.
          1-D or 2-D arrays must have the same shape.
  
      Returns
      -------
      stacked : Tensor
          The array formed by stacking the given tensors, will be at least 3-D.
  
      See Also
      --------
      stack : Join a sequence of tensors along a new axis.
      vstack : Stack along first axis.
      hstack : Stack along second axis.
      concatenate : Join a sequence of arrays along an existing axis.
      dsplit : Split tensor along third axis.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array((1,2,3))
      >>> b = mt.array((2,3,4))
      >>> mt.dstack((a,b)).execute()
      array([[[1, 2],
              [2, 3],
              [3, 4]]])
  
      >>> a = mt.array([[1],[2],[3]])
      >>> b = mt.array([[2],[3],[4]])
      >>> mt.dstack((a,b)).execute()
      array([[[1, 2]],
             [[2, 3]],
             [[3, 4]]])
  
      """
      return concatenate([atleast_3d(t) for t in tup], axis=2)
  
label_desc: Use image node_id 1 for calling a global function with example usage: dstack(tup) and returns: concatenate