tensorflow/inference/docker/build_artifacts/sagemaker/tfs_utils.py [52:106]:
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                      req.get_header(CUSTOM_ATTRIBUTES_HEADER),
                      req.get_header("Content-Type") or DEFAULT_CONTENT_TYPE,
                      req.get_header("Accept") or DEFAULT_ACCEPT_HEADER,
                      req.content_length)

    data = req.stream
    return data, context


def make_tfs_uri(port, attributes, default_model_name, model_name=None):
    log.info("sagemaker tfs attributes: \n{}".format(attributes))

    tfs_model_name = model_name or attributes.get("tfs-model-name", default_model_name)
    tfs_model_version = attributes.get("tfs-model-version")
    tfs_method = attributes.get("tfs-method", "predict")

    uri = "http://localhost:{}/v1/models/{}".format(port, tfs_model_name)
    if tfs_model_version:
        uri += "/versions/" + tfs_model_version
    uri += ":" + tfs_method
    return uri


def parse_tfs_custom_attributes(req):
    attributes = {}
    header = req.get_header(CUSTOM_ATTRIBUTES_HEADER)
    if header:
        matches = re.findall(r"(tfs-[a-z\-]+=[^,]+)", header)
        attributes = dict(attribute.split("=") for attribute in matches)
    return attributes


def create_tfs_config_individual_model(model_name, base_path):
    config = "model_config_list: {\n"
    config += "  config: {\n"
    config += "    name: '{}'\n".format(model_name)
    config += "    base_path: '{}'\n".format(base_path)
    config += "    model_platform: 'tensorflow'\n"

    config += "    model_version_policy: {\n"
    config += "      specific: {\n"
    for version in find_model_versions(base_path):
        config += "        versions: {}\n".format(version)
    config += "      }\n"
    config += "    }\n"

    config += "  }\n"
    config += "}\n"
    return config


def tfs_command(tfs_grpc_port,
                tfs_rest_port,
                tfs_config_path,
                tfs_enable_batching,
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tensorflow/inference/docker/build_artifacts/sagemaker_neuron/tfs_utils.py [45:99]:
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                      req.get_header(CUSTOM_ATTRIBUTES_HEADER),
                      req.get_header("Content-Type") or DEFAULT_CONTENT_TYPE,
                      req.get_header("Accept") or DEFAULT_ACCEPT_HEADER,
                      req.content_length)

    data = req.stream
    return data, context


def make_tfs_uri(port, attributes, default_model_name, model_name=None):
    log.info("sagemaker tfs attributes: \n{}".format(attributes))

    tfs_model_name = model_name or attributes.get("tfs-model-name", default_model_name)
    tfs_model_version = attributes.get("tfs-model-version")
    tfs_method = attributes.get("tfs-method", "predict")

    uri = "http://localhost:{}/v1/models/{}".format(port, tfs_model_name)
    if tfs_model_version:
        uri += "/versions/" + tfs_model_version
    uri += ":" + tfs_method
    return uri


def parse_tfs_custom_attributes(req):
    attributes = {}
    header = req.get_header(CUSTOM_ATTRIBUTES_HEADER)
    if header:
        matches = re.findall(r"(tfs-[a-z\-]+=[^,]+)", header)
        attributes = dict(attribute.split("=") for attribute in matches)
    return attributes


def create_tfs_config_individual_model(model_name, base_path):
    config = "model_config_list: {\n"
    config += "  config: {\n"
    config += "    name: '{}'\n".format(model_name)
    config += "    base_path: '{}'\n".format(base_path)
    config += "    model_platform: 'tensorflow'\n"

    config += "    model_version_policy: {\n"
    config += "      specific: {\n"
    for version in find_model_versions(base_path):
        config += "        versions: {}\n".format(version)
    config += "      }\n"
    config += "    }\n"

    config += "  }\n"
    config += "}\n"
    return config


def tfs_command(tfs_grpc_port,
                tfs_rest_port,
                tfs_config_path,
                tfs_enable_batching,
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