code: def test_fit_mwms(
      time,
      strftime,
      sagemaker_session,
      tensorflow_training_version,
      tensorflow_training_py_version,
  ):
      if version.Version(tensorflow_training_version) < version.Version(
          "2.11"
      ):
          pytest.skip(
              "Multi Worker Mirrored Strategy was added in TF 2.11"
          )
      framework_version = tensorflow_training_version
      py_version = tensorflow_training_py_version
      tf = TensorFlow(
          entry_point=SCRIPT_FILE,
          framework_version=framework_version,
          py_version=py_version,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_type=INSTANCE_TYPE,
          instance_count=1,
          source_dir=DATA_DIR,
          distribution=DISTRIBUTION_MWMS_ENABLED,
      )
  
      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(
          framework_version, py_version=py_version
      )
      expected_train_args["input_config"][0]["DataSource"][
          "S3DataSource"
      ]["S3Uri"] = inputs
      expected_train_args[
          "image_uri"
      ] = f"763104351884.dkr.ecr.{REGION}.amazonaws.com/tensorflow-training:{framework_version}-cpu-{py_version}"
      expected_train_args[
          "job_name"
      ] = f"tensorflow-training-{TIMESTAMP}"
      expected_train_args["hyperparameters"][
          TensorFlow.LAUNCH_MWMS_ENV_NAME
      ] = json.dumps(True)
      expected_train_args["hyperparameters"][
          "sagemaker_job_name"
      ] = json.dumps(expected_train_args["job_name"])
      expected_train_args["hyperparameters"][
          "sagemaker_submit_directory"
      ] = json.dumps(
          f"s3://{BUCKET_NAME}/{expected_train_args['job_name']}/source/sourcedir.tar.gz"
      )
      expected_train_args["hyperparameters"]["model_dir"] = json.dumps(
          f"s3://{BUCKET_NAME}/{expected_train_args['job_name']}/model"
      )
      expected_train_args["enable_sagemaker_metrics"] = True
  
      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 14 for calling a global function with example usage: test_fit_mwms(time, strftime, sagemaker_session, tensorflow_training_version, tensorflow_training_py_version) without return types