code: def _get_pytorch_estimator(
      sagemaker_session,
      pytorch_version,
      py_version,
      instance_type,
      entry_point=MNIST_SCRIPT,
      distributions_dict={},
  ):
      return PyTorch(
          entry_point=entry_point,
          role="SageMakerRole",
          framework_version=pytorch_version,
          py_version=py_version,
          instance_count=1,
          instance_type=instance_type,
          sagemaker_session=sagemaker_session,
          distributions=distributions_dict,
      )
  
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 12 for calling a global function with example usage: _get_pytorch_estimator(sagemaker_session, pytorch_version, py_version, instance_type, entry_point, distributions_dict) and returns: PyTorch