code: def unshard_leave(
      leave_shards: Iterable[np.ndarray],
      leave_name: str,
      old_shape: List[int],
      np_dtype=np.float16,
  ):
      # reshard all leave shards into single shard.
  
      # stack leave shards into single np.ndarray
      x = np.stack(leave_shards)
      # assert isinstance(x, jnp.ndarray)
  
      # As far as i can tell, this just re labels the dtype of arrays
      # labeled with "V2" dtype. In theory, V2 was just an alias for bfloat16
      # which needs to be relabeled in order for it to be understood.
      if x.dtype == np.dtype("V2"):
          x.dtype = jnp.bfloat16
  
      if DEBUG:
          print(f"RESHARDING: {leave_name=} {x.shape=} {old_shape=}")  # type: ignore
  
      # transform sharded array to match old_shape
      x = reshard(
          x,
          old_shape,
          do_shard_bias=leave_name.endswith(
              "embedding_shard/~/linear/b"
          )
          or leave_name.endswith("linear_5/b"),
          do_shard_ln=leave_name.endswith(
              "replicated_layer_norm/offset"
          )
          or leave_name.endswith("replicated_layer_norm/scale"),
      )
      assert (
          x.shape == old_shape
      ), f"Incompatible checkpoints {x.shape} vs {old_shape} {leave_name}"
      return x.astype(np_dtype)
  
imports: import argparse
  import io
  import multiprocessing
  import time
  import warnings
  import os
  import re
  from typing import Iterable, List, Union
  import json
  import jax
  import jax.numpy
  from jax.experimental import maps
  from pathy import FluidPath, Pathy
  import numpy
  import optax
  import torch
  from tqdm import tqdm
  from mesh_transformer.transformer_shard import CausalTransformer
label_desc: Use image node_id 10 for calling a global function with example usage: unshard_leave(leave_shards, leave_name, old_shape, np_dtype) and returns: x