code: def unshard(x: jnp.ndarray):
      # einops.rearrange(x, 'd b ... -> (d b) ...')
      num_devices, batch_size = x.shape[:2]
      rest = x.shape[2:]
      return x.reshape(num_devices * batch_size, *rest)
  
imports: import warnings
  from functools import partial
  from typing import Dict, List, Optional, Union
  import jax
  import jax.numpy
  import numpy
  from flax.core.frozen_dict import FrozenDict
  from flax.jax_utils import unreplicate
  from flax.training.common_utils import shard
  from PIL import Image
  from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
  from ...models import FlaxAutoencoderKL, FlaxControlNetModel, FlaxUNet2DConditionModel
  from ...schedulers import FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler
  from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
  from ..pipeline_flax_utils import FlaxDiffusionPipeline
  from ..stable_diffusion import FlaxStableDiffusionPipelineOutput
  from ..stable_diffusion.safety_checker_flax import FlaxStableDiffusionSafetyChecker
label_desc: Use image node_id 3 for calling a global function with example usage: unshard(x) and returns: x