code: def load_t5x_weights_in_t5(
      model,
      config,
      t5x_checkpoint_path,
      is_encoder_only,
      scalable_attention,
  ):
      """Replaces the params in model witht the T5X converted params."""
      variables = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
      converted = convert_t5x_to_pytorch(
          variables,
          num_layers=config.num_layers,
          is_encoder_only=is_encoder_only,
          scalable_attention=scalable_attention,
      )
      state_dict = make_state_dict(converted, is_encoder_only)
      model.load_state_dict(state_dict, strict=True)
  
file_path: transformers/src/transformers/models/umt5/convert_umt5_checkpoint_to_pytorch.py
imports: import argparse
  import collections
  import numpy
  import torch
  from flax import traverse_util
  from t5x import checkpoints
  from transformers import MT5Config, UMT5EncoderModel, UMT5ForConditionalGeneration
  from transformers.utils import logging
label_desc: Use image node_id 7 for calling a global function with example usage: load_t5x_weights_in_t5(model, config, t5x_checkpoint_path, is_encoder_only, scalable_attention) without return types