code: def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
      # convert pytorch tensor to numpy
      pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
  
      model_prefix = flax_model.base_model_prefix
      random_flax_state_dict = flatten_dict(flax_model.params)
      flax_state_dict = {}
  
      load_model_with_head_into_base_model = (
          model_prefix not in flax_model.params
      ) and (
          model_prefix
          in set([k.split(".")[0] for k in pt_state_dict.keys()])
      )
      load_base_model_into_model_with_head = (
          model_prefix in flax_model.params
      ) and (
          model_prefix
          not in set([k.split(".")[0] for k in pt_state_dict.keys()])
      )
  
      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
          pt_tuple_key = tuple(pt_key.split("."))
  
          # remove base model prefix if necessary
          has_base_model_prefix = pt_tuple_key[0] == model_prefix
          if (
              load_model_with_head_into_base_model
              and has_base_model_prefix
          ):
              pt_tuple_key = pt_tuple_key[1:]
  
          # Correctly rename weight parameters
          flax_key, flax_tensor = rename_key_and_reshape_tensor(
              pt_tuple_key,
              pt_tensor,
              random_flax_state_dict,
              model_prefix,
          )
  
          # add model prefix if necessary
          require_base_model_prefix = (
              model_prefix,
          ) + flax_key in random_flax_state_dict
          if (
              load_base_model_into_model_with_head
              and require_base_model_prefix
          ):
              flax_key = (model_prefix,) + flax_key
  
          if flax_key in random_flax_state_dict:
              if (
                  flax_tensor.shape
                  != random_flax_state_dict[flax_key].shape
              ):
                  raise ValueError(
                      f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                      f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                  )
  
          # also add unexpected weight so that warning is thrown
          flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
  
      return unflatten_dict(flax_state_dict)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/modeling_flax_pytorch_utils.py
imports: import os
  from pickle import UnpicklingError
  from typing import Dict, Tuple
  import numpy
  import jax
  import jax.numpy
  import transformers
  from flax.serialization import from_bytes
  from flax.traverse_util import flatten_dict, unflatten_dict
  from .utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model) and returns: unflatten_dict