code: def take(a, indices, axis=None, out=None):
      """
      Take elements from a tensor along an axis.
  
      When axis is not None, this function does the same thing as "fancy"
      indexing (indexing arrays using tensors); however, it can be easier to use
      if you need elements along a given axis. A call such as
      ``mt.take(arr, indices, axis=3)`` is equivalent to
      ``arr[:,:,:,indices,...]``.
  
      Explained without fancy indexing, this is equivalent to the following use
      of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
      indices::
  
          Ni, Nk = a.shape[:axis], a.shape[axis+1:]
          Nj = indices.shape
          for ii in ndindex(Ni):
              for jj in ndindex(Nj):
                  for kk in ndindex(Nk):
                      out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
  
      Parameters
      ----------
      a : array_like (Ni..., M, Nk...)
          The source tensor.
      indices : array_like (Nj...)
          The indices of the values to extract.
  
          Also allow scalars for indices.
      axis : int, optional
          The axis over which to select values. By default, the flattened
          input tensor is used.
      out : Tensor, optional (Ni..., Nj..., Nk...)
          If provided, the result will be placed in this tensor. It should
          be of the appropriate shape and dtype.
      mode : {'raise', 'wrap', 'clip'}, optional
          Specifies how out-of-bounds indices will behave.
  
          * 'raise' -- raise an error (default)
          * 'wrap' -- wrap around
          * 'clip' -- clip to the range
  
          'clip' mode means that all indices that are too large are replaced
          by the index that addresses the last element along that axis. Note
          that this disables indexing with negative numbers.
  
      Returns
      -------
      out : Tensor (Ni..., Nj..., Nk...)
          The returned tensor has the same type as `a`.
  
      See Also
      --------
      compress : Take elements using a boolean mask
      Tensor.take : equivalent method
  
      Notes
      -----
  
      By eliminating the inner loop in the description above, and using `s_` to
      build simple slice objects, `take` can be expressed  in terms of applying
      fancy indexing to each 1-d slice::
  
          Ni, Nk = a.shape[:axis], a.shape[axis+1:]
          for ii in ndindex(Ni):
              for kk in ndindex(Nj):
                  out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]
  
      For this reason, it is equivalent to (but faster than) the following use
      of `apply_along_axis`::
  
          out = mt.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)
  
      Examples
      --------
      >>> import mars.tensor as mt
      >>> a = [4, 3, 5, 7, 6, 8]
      >>> indices = [0, 1, 4]
      >>> mt.take(a, indices).execute()
      array([4, 3, 6])
  
      In this example if `a` is a tensor, "fancy" indexing can be used.
  
      >>> a = mt.array(a)
      >>> a[indices].execute()
      array([4, 3, 6])
  
      If `indices` is not one dimensional, the output also has these dimensions.
  
      >>> mt.take(a, [[0, 1], [2, 3]]).execute()
      array([[4, 3],
             [5, 7]])
      """
      a = astensor(a)
      if axis is None:
          t = a.ravel()[indices]
      else:
          axis = validate_axis(a.ndim, axis)
          t = a[(slice(None),) * axis + (indices,)]
  
      if out is None:
          return t
  
      if out.shape != t.shape:
          raise ValueError(
              f"output tensor has wrong shape, expect: {t.shape}, got: {out.shape}"
          )
      check_out_param(out, t, "unsafe")
      out.data = t.data
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: take(a, indices, axis, out) and returns: out, t