code: def reshape_to_2d_factorized(
      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: from typing import Any, Optional, Tuple
  from absl import logging
  import flax
  from flax.linen import linear
  from flax.training import common_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.model_lib.base_models import model_utils
  import scipy
label_desc: Use image node_id 2 for calling a global function with example usage: reshape_to_2d_factorized(x, axis, two_d_shape) and returns: x, x