code: def unshard(x, padding=0):
      """Collect the sharded tensor to the shape before sharding."""
      if padding > 0:
          return x.reshape(
              [x.shape[0] * x.shape[1]] + list(x.shape[2:])
          )[:-padding]
      else:
          return x.reshape(
              [x.shape[0] * x.shape[1]] + list(x.shape[2:])
          )
  
imports: import collections
  from concurrent import futures
  import contextlib
  import functools
  import time
  from typing import List, Union
  import jax
  from jax import tree_util
  import jax.numpy
  import numpy
  from scipy import interpolate
  from scipy.spatial import transform
  import tqdm
label_desc: Use image node_id 23 for calling a global function with example usage: unshard(x, padding) and returns: x, x