code: def swap_dimensions(
      method: Callable[
          [tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor]
      ]
  ) -> Callable[..., Tuple[tf.Tensor, tf.Tensor]]:
      """
      Converts between GPflow indexing and tensorflow indexing
      `method` is a function that broadcasts over the first dimension (i.e. like all tensorflow matrix
      ops):
  
      * `method` inputs [D, N, 1], [D, N, N]
      * `method` outputs [D, N, 1], [D, N, N]
  
      :return: Function that broadcasts over the final dimension (i.e. compatible with GPflow):
  
          * inputs: [N, D], [D, N, N]
          * outputs: [N, D], [D, N, N]
      """
  
      @functools.wraps(method)
      @check_shapes(
          "a_nd: [N, D] if swap",
          "a_nd: [D, N, 1] if not swap",
          "b_dnn: [D, N, N]",
          "return[0]: [N, D] if swap",
          "return[0]: [D, N, 1] if not swap",
          "return[1]: [D, N, N]",
      )
      def wrapper(
          a_nd: tf.Tensor, b_dnn: tf.Tensor, swap: bool = True
      ) -> Tuple[tf.Tensor, tf.Tensor]:
          if swap:
              a_dn1 = tf.linalg.adjoint(a_nd)[:, :, None]
              A_dn1, B_dnn = method(a_dn1, b_dnn)
              A_nd = tf.linalg.adjoint(A_dn1[:, :, 0])
              return A_nd, B_dnn
          else:
              return method(a_nd, b_dnn)
  
      return wrapper
  
imports: import abc
  import functools
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
  import tensorflow
  from check_shapes import check_shapes
  from ..base import AnyNDArray, Parameter, _to_constrained
label_desc: Use image node_id 1 for calling a global function with example usage: swap_dimensions(method) and returns: wrapper, A_nd,  B_dnn, method