code: def take_along_axis(arr, indices, axis):
      """
      Take values from the input array by matching 1d index and data slices.
  
      This iterates over matching 1d slices oriented along the specified axis in
      the index and data arrays, and uses the former to look up values in the
      latter. These slices can be different lengths.
  
      Functions returning an index along an axis, like `argsort` and
      `argpartition`, produce suitable indices for this function.
  
      .. versionadded:: 1.15.0
  
      Parameters
      ----------
      arr : ndarray (Ni..., M, Nk...)
          Source array
      indices : ndarray (Ni..., J, Nk...)
          Indices to take along each 1d slice of `arr`. This must match the
          dimension of arr, but dimensions Ni and Nj only need to broadcast
          against `arr`.
      axis : int
          The axis to take 1d slices along. If axis is None, the input array is
          treated as if it had first been flattened to 1d, for consistency with
          `sort` and `argsort`.
  
      Returns
      -------
      out: ndarray (Ni..., J, Nk...)
          The indexed result.
  
      Notes
      -----
      This is equivalent to (but faster than) the following use of `ndindex` and
      `s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices::
  
          Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
          J = indices.shape[axis]  # Need not equal M
          out = np.empty(Ni + (J,) + Nk)
  
          for ii in ndindex(Ni):
              for kk in ndindex(Nk):
                  a_1d       = a      [ii + s_[:,] + kk]
                  indices_1d = indices[ii + s_[:,] + kk]
                  out_1d     = out    [ii + s_[:,] + kk]
                  for j in range(J):
                      out_1d[j] = a_1d[indices_1d[j]]
  
      Equivalently, eliminating the inner loop, the last two lines would be::
  
                  out_1d[:] = a_1d[indices_1d]
  
      See Also
      --------
      take : Take along an axis, using the same indices for every 1d slice
      put_along_axis :
          Put values into the destination array by matching 1d index and data slices
  
      Examples
      --------
  
      For this sample array
  
      >>> a = np.array([[10, 30, 20], [60, 40, 50]])
  
      We can sort either by using sort directly, or argsort and this function
  
      >>> np.sort(a, axis=1)
      array([[10, 20, 30],
             [40, 50, 60]])
      >>> ai = np.argsort(a, axis=1)
      >>> ai
      array([[0, 2, 1],
             [1, 2, 0]])
      >>> np.take_along_axis(a, ai, axis=1)
      array([[10, 20, 30],
             [40, 50, 60]])
  
      The same works for max and min, if you maintain the trivial dimension
      with ``keepdims``:
  
      >>> np.max(a, axis=1, keepdims=True)
      array([[30],
             [60]])
      >>> ai = np.argmax(a, axis=1, keepdims=True)
      >>> ai
      array([[1],
             [0]])
      >>> np.take_along_axis(a, ai, axis=1)
      array([[30],
             [60]])
  
      If we want to get the max and min at the same time, we can stack the
      indices first
  
      >>> ai_min = np.argmin(a, axis=1, keepdims=True)
      >>> ai_max = np.argmax(a, axis=1, keepdims=True)
      >>> ai = np.concatenate([ai_min, ai_max], axis=1)
      >>> ai
      array([[0, 1],
             [1, 0]])
      >>> np.take_along_axis(a, ai, axis=1)
      array([[10, 30],
             [40, 60]])
      """
      # normalize inputs
      if axis is None:
          arr = arr.flat
          arr_shape = (len(arr),)  # flatiter has no .shape
          axis = 0
      else:
          axis = normalize_axis_index(axis, arr.ndim)
          arr_shape = arr.shape
  
      # use the fancy index
      return arr[_make_along_axis_idx(arr_shape, indices, axis)]
  
imports: import functools
  import numpy.core.numeric
  from numpy.core.numeric import asarray, zeros, array, asanyarray
  from numpy.core.fromnumeric import reshape, transpose
  from numpy.core.multiarray import normalize_axis_index
  from numpy.core import overrides
  from numpy.core import vstack, atleast_3d
  from numpy.core.numeric import normalize_axis_tuple
  from numpy.core.shape_base import _arrays_for_stack_dispatcher
  from numpy.lib.index_tricks import ndindex
  from numpy.matrixlib.defmatrix import matrix
label_desc: Use image node_id 3 for calling a global function with example usage: take_along_axis(arr, indices, axis) and returns: arr