code: def median(
      a, axis=None, out=None, overwrite_input=False, keepdims=False
  ):
      """
      Compute the median along the specified axis.
  
      Returns the median of the tensor elements.
  
      Parameters
      ----------
      a : array_like
          Input tensor or object that can be converted to a tensor.
      axis : {int, sequence of int, None}, optional
          Axis or axes along which the medians are computed. The default
          is to compute the median along a flattened version of the tensor.
          A sequence of axes is supported since version 1.9.0.
      out : Tensor, optional
          Alternative output tensor in which to place the result. It must
          have the same shape and buffer length as the expected output,
          but the type (of the output) will be cast if necessary.
      overwrite_input : bool, optional
          Just for compatibility with Numpy, would not take effect.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      median : Tensor
          A new tensor holding the result. If the input contains integers
          or floats smaller than ``float64``, then the output data-type is
          ``np.float64``.  Otherwise, the data-type of the output is the
          same as that of the input. If `out` is specified, that tensor is
          returned instead.
  
      See Also
      --------
      mean, percentile
  
      Notes
      -----
      Given a vector ``V`` of length ``N``, the median of ``V`` is the
      middle value of a sorted copy of ``V``, ``V_sorted`` - i
      e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
      two middle values of ``V_sorted`` when ``N`` is even.
  
      Examples
      --------
      >>> import mars.tensor as mt
      >>> a = mt.array([[10, 7, 4], [3, 2, 1]])
      >>> a.execute()
      array([[10,  7,  4],
             [ 3,  2,  1]])
      >>> mt.median(a).execute()
      3.5
      >>> mt.median(a, axis=0).execute()
      array([6.5, 4.5, 2.5])
      >>> mt.median(a, axis=1).execute()
      array([7.,  2.])
      >>> m = mt.median(a, axis=0)
      >>> out = mt.zeros_like(m)
      >>> mt.median(a, axis=0, out=m).execute()
      array([6.5,  4.5,  2.5])
      >>> m.execute()
      array([6.5,  4.5,  2.5])
      """
      return quantile(
          a,
          0.5,
          axis=axis,
          out=out,
          overwrite_input=overwrite_input,
          keepdims=keepdims,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: median(a, axis, out, overwrite_input, keepdims) and returns: quantile