5. MLOps SageMaker Project/sagemaker-workshop-deploy-seedcode-v1/pipelines/deploy/pipeline.py [41:95]:
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def get_sagemaker_client(region):
    """Gets the sagemaker client.

        Args:
            region: the aws region to start the session
            default_bucket: the bucket to use for storing the artifacts

        Returns:
            `sagemaker.session.Session instance
    """
    boto_session = boto3.Session(region_name=region)
    sagemaker_client = boto_session.client("sagemaker")
    return sagemaker_client


def get_session(region, default_bucket):
    """Gets the sagemaker session based on the region.

    Args:
        region: the aws region to start the session
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        `sagemaker.session.Session instance
    """

    boto_session = boto3.Session(region_name=region)

    sagemaker_client = boto_session.client("sagemaker")
    runtime_client = boto_session.client("sagemaker-runtime")
    return sagemaker.session.Session(
        boto_session=boto_session,
        sagemaker_client=sagemaker_client,
        sagemaker_runtime_client=runtime_client,
        default_bucket=default_bucket,
    )

# def get_pipeline_custom_tags(new_tags, region, sagemaker_project_arn=None):
#     try:
#         sm_client = get_sagemaker_client(region)
#         response = sm_client.list_tags(
#             ResourceArn=sagemaker_project_arn)
#         project_tags = response["Tags"]
#         for project_tag in project_tags:
#             new_tags.append(project_tag)
#     except Exception as e:
#         print(f"Error getting project tags: {e}")
#     return new_tags


def get_pipeline(
    region,
    sagemaker_project_arn=None,
    role=None,
    default_bucket=None,
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5. MLOps SageMaker Project/sagemaker-workshop-preprocess-seedcode-v1/pipelines/preprocess/pipeline.py [32:86]:
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def get_sagemaker_client(region):
    """Gets the sagemaker client.

        Args:
            region: the aws region to start the session
            default_bucket: the bucket to use for storing the artifacts

        Returns:
            `sagemaker.session.Session instance
    """
    boto_session = boto3.Session(region_name=region)
    sagemaker_client = boto_session.client("sagemaker")
    return sagemaker_client


def get_session(region, default_bucket):
    """Gets the sagemaker session based on the region.

    Args:
        region: the aws region to start the session
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        `sagemaker.session.Session instance
    """

    boto_session = boto3.Session(region_name=region)

    sagemaker_client = boto_session.client("sagemaker")
    runtime_client = boto_session.client("sagemaker-runtime")
    return sagemaker.session.Session(
        boto_session=boto_session,
        sagemaker_client=sagemaker_client,
        sagemaker_runtime_client=runtime_client,
        default_bucket=default_bucket,
    )

# def get_pipeline_custom_tags(new_tags, region, sagemaker_project_arn=None):
#     try:
#         sm_client = get_sagemaker_client(region)
#         response = sm_client.list_tags(
#             ResourceArn=sagemaker_project_arn)
#         project_tags = response["Tags"]
#         for project_tag in project_tags:
#             new_tags.append(project_tag)
#     except Exception as e:
#         print(f"Error getting project tags: {e}")
#     return new_tags


def get_pipeline(
    region,
    sagemaker_project_arn=None,
    role=None,
    default_bucket=None,
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