code: def data_path():
      # Generate 100 samples, each has 7 features and 1 label
      # feature value in range [0,50)
      # label value in range [0,2)
      # predicted label, float value in range [0,1)
      features = np.random.randint(50, size=(100, 7))
      label = np.random.randint(2, size=100)
      predicted_label = np.random.rand(100)
      data = pd.concat(
          [
              pd.DataFrame(features),
              pd.DataFrame(label),
              pd.DataFrame(predicted_label),
          ],
          axis=1,
          sort=False,
      )
      with tempfile.TemporaryDirectory() as tmpdirname:
          filename = os.path.join(tmpdirname, "train.csv")
          data.to_csv(filename, index=False, header=ALL_HEADERS)
          yield filename
  
imports: from __future__ import print_function, absolute_import
  import tempfile
  from datetime import datetime, timedelta
  import json
  import os
  import numpy
  import pytest
  import pandas
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import CronExpressionGenerator, DataCaptureConfig, EndpointInput, ModelQualityMonitor, DatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 3 for calling a global function with example usage: data_path() without return types