code: def predict_cpu(model_dir, dataset):
      model = LudwigModel.load(model_dir, backend="local")
      model.predict(dataset)
  
imports: import copy
  import os
  import tempfile
  from typing import Literal, Optional, Union
  import numpy
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.backend import create_ray_backend, initialize_backend, LOCAL_BACKEND
  from ludwig.constants import AUDIO, BAG, BALANCE_PERCENTAGE_TOLERANCE, BATCH_SIZE, BFILL, BINARY, CATEGORY, COLUMN, DATE, H3, IMAGE, MAX_BATCH_SIZE_DATASET_FRACTION, NAME, NUMBER, PREPROCESSING, SEQUENCE, SET, SPLIT, TEXT, TIMESERIES, TRAINER, VECTOR
  from ludwig.data.preprocessing import balance_data
  from ludwig.data.split import DEFAULT_PROBABILITIES
  from ludwig.utils.data_utils import read_parquet
  from ludwig.utils.misc_utils import merge_dict
  from tests.integration_tests.utils import audio_feature, augment_dataset_with_none, bag_feature, binary_feature, category_feature, create_data_set_to_use, date_feature, generate_data, h3_feature, image_feature, number_feature, RAY_BACKEND_CONFIG, sequence_feature, set_feature, text_feature, timeseries_feature, train_with_backend, vector_feature
  import dask
  import ray
  import ray.exceptions
  from ray.air.config import DatasetConfig
  from ray.data import Dataset, DatasetPipeline
  from ray.train._internal.dataset_spec import DataParallelIngestSpec
  from ludwig.backend.ray import get_trainer_kwargs, RayBackend
  from ludwig.data.dataframe.dask import DaskEngine
label_desc: Use image node_id 2 for calling a global function with example usage: predict_cpu(model_dir, dataset) without return types