parameters: (arr, indices, values, axis)
docstring: Put values into the destination 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 place values into 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...)
          Destination array.
      indices : ndarray (Ni..., J, Nk...)
          Indices to change along each 1d slice of `arr`. This must match the
          dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast
          against `arr`.
      values : array_like (Ni..., J, Nk...)
          values to insert at those indices. Its shape and dimension are
          broadcast to match that of `indices`.
      axis : int
          The axis to take 1d slices along. If axis is None, the destination
          array is treated as if a flattened 1d view had been created of it.
  
      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
  
          for ii in ndindex(Ni):
              for kk in ndindex(Nk):
                  a_1d       = a      [ii + s_[:,] + kk]
                  indices_1d = indices[ii + s_[:,] + kk]
                  values_1d  = values [ii + s_[:,] + kk]
                  for j in range(J):
                      a_1d[indices_1d[j]] = values_1d[j]
  
      Equivalently, eliminating the inner loop, the last two lines would be::
  
                  a_1d[indices_1d] = values_1d
  
      See Also
      --------
      take_along_axis :
          Take values from the input array by matching 1d index and data slices
  
      Examples
      --------
  
      For this sample array
  
      >>> a = np.array([[10, 30, 20], [60, 40, 50]])
  
      We can replace the maximum values with:
  
      >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
      >>> ai
      array([[1],
             [0]])
      >>> np.put_along_axis(a, ai, 99, axis=1)
      >>> a
      array([[10, 99, 20],
             [99, 40, 50]])
docstring_summary: Put values into the destination array by matching 1d index and data slices.
docstring_tokens: Put values into the destination array by matching 1d index and data slices .
function: def put_along_axis(arr, indices, values, axis):
      """
      Put values into the destination 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 place values into 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...)
          Destination array.
      indices : ndarray (Ni..., J, Nk...)
          Indices to change along each 1d slice of `arr`. This must match the
          dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast
          against `arr`.
      values : array_like (Ni..., J, Nk...)
          values to insert at those indices. Its shape and dimension are
          broadcast to match that of `indices`.
      axis : int
          The axis to take 1d slices along. If axis is None, the destination
          array is treated as if a flattened 1d view had been created of it.
  
      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
  
          for ii in ndindex(Ni):
              for kk in ndindex(Nk):
                  a_1d       = a      [ii + s_[:,] + kk]
                  indices_1d = indices[ii + s_[:,] + kk]
                  values_1d  = values [ii + s_[:,] + kk]
                  for j in range(J):
                      a_1d[indices_1d[j]] = values_1d[j]
  
      Equivalently, eliminating the inner loop, the last two lines would be::
  
                  a_1d[indices_1d] = values_1d
  
      See Also
      --------
      take_along_axis :
          Take values from the input array by matching 1d index and data slices
  
      Examples
      --------
  
      For this sample array
  
      >>> a = np.array([[10, 30, 20], [60, 40, 50]])
  
      We can replace the maximum values with:
  
      >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
      >>> ai
      array([[1],
             [0]])
      >>> np.put_along_axis(a, ai, 99, axis=1)
      >>> a
      array([[10, 99, 20],
             [99, 40, 50]])
  
      """
      # normalize inputs
      if axis is None:
          arr = arr.flat
          axis = 0
          arr_shape = (len(arr),)  # flatiter has no .shape
      else:
          axis = normalize_axis_index(axis, arr.ndim)
          arr_shape = arr.shape
  
      # use the fancy index
      arr[_make_along_axis_idx(arr_shape, indices, axis)] = values
function_tokens: def put_along_axis ( arr , indices , values , axis ) : # normalize inputs if axis is None : arr = arr . flat axis = 0 arr_shape = ( len ( arr ) , ) # flatiter has no .shape else : axis = normalize_axis_index ( axis , arr . ndim ) arr_shape = arr . shape # use the fancy index arr [ _make_along_axis_idx ( arr_shape , indices , axis ) ] = values