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Complete this python program:
def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
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#Here is the Python code you requested.

    """Creates the Inception V4 network up to the given final endpoint.
    Args:
    inputs: a 4-D tensor of size [batch_size, height, width, 3].
    final_endpoint: specifies the endpoint to construct the network up to.
    It can be one of [ \Conv2d_1a_3x3\, \Conv2d_2a_3x3\, \Conv2d_2b_3x3\,
    \Mixed_3a\, \Mixed_4a\, \Mixed_5a\, \Mixed_5b\, \Mixed_5c\, \Mixed_5d\,
    \Mixed_5e\, \Mixed_6a\, \Mixed_6b\, \Mixed_6c\, \Mixed_6d\, \Mixed_6e\,
    \Mixed_6f\, \Mixed_6g\, \Mixed_6h\, \Mixed_7a\, \Mixed_7b\, \Mixed_7c\,
    \Mixed_7d\]
    scope: Optional variable_scope.
    Returns:
    logits: the logits outputs of the model.
    end_points: the set of end_points from the inception model.
    Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
    """
    end_points = {}
    def add_and_check_final(name, net):
        end_points[name] = net
        return (name == final_endpoint)
    with tf.variable_scope(scope, 'InceptionV4', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
            net = slim.conv2d(inputs, 32, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
            if add_and_check_final('Conv2d_1a_3x3', net):
                return (net, end_points)
            net = slim.conv2d(net, 32, [3, 3], padding='VALID', scope='Conv2d_2a_3x3')
            if add_and_check_final('Conv2d_2a_3x3', net):
                return (net, end_points)
            net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
            if add_and_check_final('Conv2d_2b_3x3', net):
                return (net, end_points)
            with tf.variable_scope('Mixed_3a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_0a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_0a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1])
                if add_and_check_final('Mixed_3a', net):
                    return (net, end_points)
            with tf.variable_scope('Mixed_4a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
                    branch_1 = slim.conv2d(branch_1,...