code: def load_model_dict_from_path(path):
      # Ensure that the path is a string
      assert isinstance(
          path, str
      ), "Path must be a string, but got {}".format(type(path))
  
      # Append the filename to the path
      model_path = os.path.join(path, "pytorch_model.bin")
  
      # Check if the file exists
      if not os.path.isfile(model_path):
          raise FileNotFoundError(
              f"No 'pytorch_model.bin' file found at {model_path}, no saved model found"
          )
  
      # Load the state dict from the specified path
      model_dict = t.load(model_path)
  
      return model_dict
  
imports: import os
  import torch
  import torch
  import logging
  import numpy
  from torch import optim, nn
  import pandas
  from typing import Literal, Type, Callable
  from fate.ml.nn.trainer.trainer_base import TrainingArguments
  from fate.components.core import Role
  from fate.arch import Context
  from typing import Optional, Union
  from transformers.trainer_utils import PredictionOutput
  from fate.arch.dataframe._dataframe import DataFrame
  from fate.components.components.utils import consts
  from torch.optim.lr_scheduler import _LRScheduler
  from fate.ml.utils.predict_tools import to_dist_df, array_to_predict_df
  from fate.ml.utils.predict_tools import BINARY, MULTI, REGRESSION, OTHER, LABEL, PREDICT_SCORE
  from fate.components.components.nn.loader import Loader
label_desc: Use image node_id 3 for calling a global function with example usage: load_model_dict_from_path(path) and returns: model_dict