code: def atleast_2d(*tensors):
      """
      View inputs as tensors with at least two dimensions.
  
      Parameters
      ----------
      tensors1, tensors2, ... : array_like
          One or more array-like sequences.  Non-tensor inputs are converted
          to tensors.  Tensors that already have two or more dimensions are
          preserved.
  
      Returns
      -------
      res, res2, ... : Tensor
          A tensor, or list of tensors, each with ``a.ndim >= 2``.
          Copies are avoided where possible, and views with two or more
          dimensions are returned.
  
      See Also
      --------
      atleast_1d, atleast_3d
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.atleast_2d(3.0).execute()
      array([[ 3.]])
  
      >>> x = mt.arange(3.0)
      >>> mt.atleast_2d(x).execute()
      array([[ 0.,  1.,  2.]])
  
      >>> mt.atleast_2d(1, [1, 2], [[1, 2]]).execute()
      [array([[1]]), array([[1, 2]]), array([[1, 2]])]
  
      """
      new_tensors = []
      for x in tensors:
          x = astensor(x)
          if x.ndim == 0:
              x = x[np.newaxis, np.newaxis]
          elif x.ndim == 1:
              x = x[np.newaxis, :]
  
          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_2d() and returns: ExecutableTuple, new_tensors