code: def test_fit(time, strftime, sagemaker_session):
      tf = TensorFlow(
          entry_point=SCRIPT_FILE,
          framework_version="1.11",
          py_version="py2",
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_type=INSTANCE_TYPE,
          instance_count=1,
          source_dir=DATA_DIR,
      )
  
      inputs = "s3://mybucket/train"
      tf.fit(inputs=inputs)
  
      call_names = [c[0] for c in sagemaker_session.method_calls]
      assert call_names == ["train", "logs_for_job"]
  
      expected_train_args = _create_train_job("1.11", py_version="py2")
      expected_train_args["input_config"][0]["DataSource"][
          "S3DataSource"
      ]["S3Uri"] = inputs
  
      actual_train_args = sagemaker_session.method_calls[0][2]
      assert actual_train_args == expected_train_args
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  from mock import patch, Mock, MagicMock
  from packaging import version
  import pytest
  from sagemaker.estimator import _TrainingJob
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.workflow.parameters import ParameterString, ParameterBoolean
  from tests.unit import DATA_DIR
label_desc: Use image node_id 11 for calling a global function with example usage: test_fit(time, strftime, sagemaker_session) without return types