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)
      features = np.random.randint(50, size=(100, 7))
      label = np.random.randint(2, size=100)
      data = pd.concat(
          [pd.DataFrame(features), pd.DataFrame(label)],
          axis=1,
          sort=False,
      )
      with tempfile.TemporaryDirectory() as tmpdirname:
          filename = os.path.join(tmpdirname, "train.csv")
          data.to_csv(filename, index=False, header=False)
          yield filename
  
imports: from __future__ import print_function, absolute_import
  from datetime import datetime, timedelta
  import json
  import numpy
  import os
  import pandas
  import pytest
  import tempfile
  import uuid
  import time
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import BiasAnalysisConfig, CronExpressionGenerator, DataCaptureConfig, EndpointInput, ExplainabilityAnalysisConfig, ModelBiasMonitor, ModelExplainabilityMonitor, BatchTransformInput, MonitoringDatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 7 for calling a global function with example usage: data_path() without return types