code: def reshape_to_1d_factorized(x: jnp.ndarray, axis: int):
      """Converts 2d inputs to 1d for axial attention."""
  
      assert x.ndim == 4, (
          "The input dimention should be "
          "[batch_size, height, width, channel]"
      )
      batch_size, height, width, channel = x.shape
      if axis == 1:
          return x.transpose((0, 2, 1, 3)).reshape(
              batch_size * width, height, channel
          )
      elif axis == 2:
          return x.reshape(batch_size * height, width, channel)
  
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 1 for calling a global function with example usage: reshape_to_1d_factorized(x, axis) and returns: x, x