code: def get_axial_2d_input(
      x: jnp.ndarray, axis: int, two_d_shape: Tuple[int, int, int, int]
  ):
      """Converts 1d inputs back to 2d after axial attention."""
      assert x.ndim == 3, (
          "The input dimention should be "
          "[batch_size, height*width, channel]"
      )
      batch_size, height, width, channel = two_d_shape
      if axis == 1:
          assert x.shape[0] == batch_size * width
          return x.reshape(
              (batch_size, width, height, channel)
          ).transpose((0, 2, 1, 3))
      elif axis == 2:
          assert x.shape[0] == batch_size * height
          return x.reshape(two_d_shape)
  
imports: import abc
  import enum
  import functools
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Iterable
  from absl import logging
  from flax import linen
  import jax
  from jax import lax
  from jax import random
  import jax.numpy
  import numpy
  from scenic.model_lib.layers import attention_layers
label_desc: Use image node_id 4 for calling a global function with example usage: get_axial_2d_input(x, axis, two_d_shape) and returns: x, x