tf-batch-inference-script/code/inference.py [20:65]:
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Context = namedtuple('Context',
                     'model_name, model_version, method, rest_uri, grpc_uri, '
                     'custom_attributes, request_content_type, accept_header')


def input_handler(data, context):
    """ Pre-process request input before it is sent to TensorFlow Serving REST API

    Args:
        data (obj): the request data, in format of dict or string
        context (Context): an object containing request and configuration details

    Returns:
        (dict): a JSON-serializable dict that contains request body and headers
    """

    if context.request_content_type == 'application/x-image':

        image_as_bytes = io.BytesIO(data.read())
        image = Image.open(image_as_bytes)
        instance = np.expand_dims(image, axis=0)
        return json.dumps({"instances": instance.tolist()})

    else:
        _return_error(415, 'Unsupported content type "{}"'.format(context.request_content_type or 'Unknown'))


def output_handler(data, context):
    """Post-process TensorFlow Serving output before it is returned to the client.

    Args:
        data (obj): the TensorFlow serving response
        context (Context): an object containing request and configuration details

    Returns:
        (bytes, string): data to return to client, response content type
    """
    if data.status_code != 200:
        raise Exception(data.content.decode('utf-8'))
    response_content_type = context.accept_header
    prediction = data.content
    return prediction, response_content_type


def _return_error(code, message):
    raise ValueError('Error: {}, {}'.format(str(code), message))
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tf-distribution-options/code/inference.py [20:65]:
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Context = namedtuple('Context',
                     'model_name, model_version, method, rest_uri, grpc_uri, '
                     'custom_attributes, request_content_type, accept_header')


def input_handler(data, context):
    """ Pre-process request input before it is sent to TensorFlow Serving REST API

    Args:
        data (obj): the request data, in format of dict or string
        context (Context): an object containing request and configuration details

    Returns:
        (dict): a JSON-serializable dict that contains request body and headers
    """

    if context.request_content_type == 'application/x-image':

        image_as_bytes = io.BytesIO(data.read())
        image = Image.open(image_as_bytes)
        instance = np.expand_dims(image, axis=0)
        return json.dumps({"instances": instance.tolist()})

    else:
        _return_error(415, 'Unsupported content type "{}"'.format(context.request_content_type or 'Unknown'))


def output_handler(data, context):
    """Post-process TensorFlow Serving output before it is returned to the client.

    Args:
        data (obj): the TensorFlow serving response
        context (Context): an object containing request and configuration details

    Returns:
        (bytes, string): data to return to client, response content type
    """
    if data.status_code != 200:
        raise Exception(data.content.decode('utf-8'))
    response_content_type = context.accept_header
    prediction = data.content
    return prediction, response_content_type


def _return_error(code, message):
    raise ValueError('Error: {}, {}'.format(str(code), message))
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