code: def save_pytree_as_hf(
      pytree,
      input_ckpt: FluidPath,
      shards_in: int,
      output_path: FluidPath,
      n_layers: int = 28,
      np_dtype: type = np.float16,
      torch_dtype: torch.dtype = torch.float16,
      n_seq: int = 2048,
  ):
      # Loads layers and names in reverse order to avoid loading unneeded opt_state layers
      # that are at the front of full (i.e. not slim) models.
  
      old_leave_shapes = [
          old.shape for old in jax.tree_flatten(pytree)[0]
      ]
      leave_names = get_tree_leaves_names_reduced(pytree)
      del pytree
  
      assert len(old_leave_shapes) == len(
          leave_names
      ), f"{len(old_leave_shapes)=}  {len(leave_names)=}"
      # get generator that emits all shards of leaves from npz files in reverse order
      loaded_shards_in = lazy_read_ckpt_shards(
          input_ckpt, shards_in, reverse=True
      )
  
      print(
          "Reading and transforming layers/shards. This may take a while."
      )
  
      hf_checkpoint = {}
      wte_first = None  # saves first instance of a wte weight in order to combine it with the second.
      # Reverse iteration to grab leave_names and old leaves from the back
      for i in tqdm(
          reversed(range(len(leave_names))),
          desc="Reading/Transforming Layers",
          total=len(leave_names),
      ):
          # load next shard with correstponding leave name and old shape
          x = next(loaded_shards_in)
          leave_name = leave_names[i]
          old_shape = old_leave_shapes[i]
          hf_layer_id = leave_name_to_hf_layer_id(leave_name)
  
          # If leave is not needed in hf model (/step')
          if not hf_layer_id:
              continue
  
          x = unshard_leave(x, leave_name, old_shape, np_dtype=np_dtype)
          # remove first empty dimension and transpose.
          x = torch.tensor(x.squeeze(0), dtype=torch_dtype).T
  
          # wte embedding weights/bias need to be combined since hf model has no wte.embedding.bias
          if hf_layer_id.startswith("transformer.wte"):
              # un/re-transpose since wte weight is only leave that shouldn't be transposed
              x = x.T
              # store first weight/bias then skip saving
              if wte_first is None:
                  wte_first = x
                  continue
              # combine second wte bias/weight with first then move on to saving with weight name
              else:
                  x = x + wte_first
                  hf_layer_id = "transformer.wte.weight"
  
          # save params as single file with proper hf id mapped to them in save_map
          hf_checkpoint[hf_layer_id] = x
  
      # add attention bias layers
      attn_bias_weights = torch.tril(
          torch.ones((n_seq, n_seq), dtype=torch.bool)
      ).view(1, 1, n_seq, n_seq)
      attn_masked_bias_weights = torch.tensor(-1e9, dtype=torch_dtype)
  
      for i in range(n_layers):
          hf_checkpoint[
              f"transformer.h.{i}.attn.bias"
          ] = attn_bias_weights
          hf_checkpoint[
              f"transformer.h.{i}.attn.masked_bias"
          ] = attn_masked_bias_weights
  
      torch.save(
          hf_checkpoint,
          (output_path / "pytorch_model.bin").open(mode="wb"),
      )
  
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 11 for calling a global function with example usage: save_pytree_as_hf(pytree, input_ckpt, shards_in, output_path, n_layers, np_dtype, torch_dtype, n_seq) without return types