code: def atleast_1d(*tensors):
      """
      Convert inputs to tensors with at least one dimension.
  
      Scalar inputs are converted to 1-dimensional tensors, whilst
      higher-dimensional inputs are preserved.
  
      Parameters
      ----------
      tensors1, tensors2, ... : array_like
          One or more input tensors.
  
      Returns
      -------
      ret : Tensor
          An tensor, or list of tensors, each with ``a.ndim >= 1``.
          Copies are made only if necessary.
  
      See Also
      --------
      atleast_2d, atleast_3d
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.atleast_1d(1.0).execute()
      array([ 1.])
  
      >>> x = mt.arange(9.0).reshape(3,3)
      >>> mt.atleast_1d(x).execute()
      array([[ 0.,  1.,  2.],
             [ 3.,  4.,  5.],
             [ 6.,  7.,  8.]])
      >>> mt.atleast_1d(x) is x
      True
  
      >>> mt.atleast_1d(1, [3, 4]).execute()
      [array([1]), array([3, 4])]
  
      """
      new_tensors = []
      for x in tensors:
          x = astensor(x)
          if x.ndim == 0:
              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_1d() and returns: ExecutableTuple, new_tensors