code: def atleast_3d(*tensors):
      """
      View inputs as tensors with at least three dimensions.
  
      Parameters
      ----------
      tensors1, tensors2, ... : array_like
          One or more tensor-like sequences.  Non-tensor inputs are converted to
          tensors.  Tensors that already have three or more dimensions are
          preserved.
  
      Returns
      -------
      res1, res2, ... : Tensor
          A tensor, or list of tensors, each with ``a.ndim >= 3``.  Copies are
          avoided where possible, and views with three or more dimensions are
          returned.  For example, a 1-D tensor of shape ``(N,)`` becomes a view
          of shape ``(1, N, 1)``, and a 2-D tensor of shape ``(M, N)`` becomes a
          view of shape ``(M, N, 1)``.
  
      See Also
      --------
      atleast_1d, atleast_2d
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.atleast_3d(3.0).execute()
      array([[[ 3.]]])
  
      >>> x = mt.arange(3.0)
      >>> mt.atleast_3d(x).shape
      (1, 3, 1)
  
      >>> x = mt.arange(12.0).reshape(4,3)
      >>> mt.atleast_3d(x).shape
      (4, 3, 1)
  
      >>> for arr in mt.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]).execute():
      ...     print(arr, arr.shape)
      ...
      [[[1]
        [2]]] (1, 2, 1)
      [[[1]
        [2]]] (1, 2, 1)
      [[[1 2]]] (1, 1, 2)
  
      """
      new_tensors = []
      for x in tensors:
          x = astensor(x)
          if x.ndim == 0:
              x = x[np.newaxis, np.newaxis, np.newaxis]
          elif x.ndim == 1:
              x = x[np.newaxis, :, np.newaxis]
          elif x.ndim == 2:
              x = x[:, :, None]
  
          new_tensors.append(x)
  
      if len(new_tensors) == 1:
          return new_tensors[0]
      return ExecutableTuple(new_tensors)
  
imports: import numpy
  from ...core import ExecutableTuple
  from ..datasource import tensor
label_desc: Use image node_id 1 for calling a global function with example usage: atleast_3d() and returns: ExecutableTuple, new_tensors