code: def test_fit_deploy(
      pytorch_training_job_with_latest_infernce_version,
      sagemaker_session,
      cpu_instance_type,
  ):
      endpoint_name = unique_name_from_base(
          "test-pytorch-sync-fit-attach-deploy"
      )
      with timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          estimator = PyTorch.attach(
              pytorch_training_job_with_latest_infernce_version,
              sagemaker_session=sagemaker_session,
          )
          predictor = estimator.deploy(
              1, cpu_instance_type, endpoint_name=endpoint_name
          )
          data = numpy.zeros(shape=(1, 1, 28, 28), dtype=numpy.float32)
          predictor.predict(data)
  
          batch_size = 100
          data = numpy.random.rand(batch_size, 1, 28, 28).astype(
              numpy.float32
          )
          output = predictor.predict(data)
  
          assert output.shape == (batch_size, 10)
  
imports: from __future__ import absolute_import
  import numpy
  import os
  import pytest
  from sagemaker.pytorch.estimator import PyTorch
  from sagemaker.pytorch.model import PyTorchModel
  from sagemaker.pytorch.processing import PyTorchProcessor
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.utils import unique_name_from_base
  from tests.integ import test_region, DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, EI_SUPPORTED_REGIONS
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_deploy(pytorch_training_job_with_latest_infernce_version, sagemaker_session, cpu_instance_type) without return types