code: def estimator_tf(sagemaker_session):
      return TensorFlow(
          entry_point="/some/script.py",
          framework_version="1.15.2",
          py_version="py3",
          role=ROLE,
          instance_type="ml.c4.2xlarge",
          instance_count=1,
          sagemaker_session=sagemaker_session,
      )
  
imports: from __future__ import absolute_import
  import json
  import os
  import tempfile
  import shutil
  import pytest
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.workflow._utils import REPACK_SCRIPT_LAUNCHER
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionEquals
  from sagemaker.workflow.model_step import ModelStep, _CREATE_MODEL_NAME_BASE, _REPACK_MODEL_NAME_BASE
  from sagemaker.workflow.parameters import ParameterString
  from sagemaker.workflow.pipeline import Pipeline, PipelineGraph
  from sagemaker.workflow.utilities import list_to_request
  from tests.unit import DATA_DIR
  from sagemaker import PipelineModel
  from sagemaker.estimator import Estimator
  from sagemaker.model import Model, FrameworkModel
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.inputs import CreateModelInput, TransformInput
  from sagemaker.model_metrics import MetricsSource, ModelMetrics
  from sagemaker.workflow.properties import Properties
  from sagemaker.workflow.steps import CreateModelStep
  from sagemaker.workflow.step_collections import EstimatorTransformer, StepCollection, RegisterModel
  from sagemaker.workflow.retry import StepRetryPolicy, StepExceptionTypeEnum
  from tests.unit.sagemaker.workflow.helpers import ordered, CustomStep
  from tests.unit.sagemaker.workflow.conftest import IMAGE_URI, ROLE, BUCKET, REGION
label_desc: Use image node_id 5 for calling a global function with example usage: estimator_tf(sagemaker_session) and returns: TensorFlow