human
Complete the below
def inception_resnet_v2_base(inputs, final_endpoint='Conv2d_7b_1x1', output_stride=16, align_feature_maps=False, scope=None):

    ''''Inception model from  http://arxiv.org/abs/1602.07261.
    Constructs an Inception Resnet v2 network from inputs to the given final
    endpoint. This method can construct the network up to the final inception
    block Conv2d_7b_1x1.
    Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    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\',
    \'MaxPool_3a_3x3\', \'Conv2d_3b_1x1\', \'Conv2d_4a_3x3\', \'MaxPool_5a_3x3\',
    \'Mixed_5b\', \'Mixed_6a\', \'PreAuxLogits\', \'Mixed_7a\', \'Conv2d_7b_1x1\']
    output_stride: A scalar that specifies the requested ratio of input to
    output spatial resolution. Only supports 8 and 16.
    align_feature_maps: When true, changes all the VALID paddings in the network
    to SAME padding so that the feature maps are aligned.
    scope: Optional variable_scope.
    Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
    losses.
    Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
    or if the output_stride is not 8 or 16, or if the output_stride is 8 and
    we request an end point after \'PreAuxLogits\'.'
    '''

Give me a the Python code you requested?
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#Here is the Python code you requested.
    if ((output_stride != 8) and (output_stride != 16)):
        raise ValueError('output_stride    must    be    8    or    16.')
    padding = ('SAME' if align_feature_maps else 'VALID')
    end_points = {}
    def add_and_check_final(name, net):
        end_points[name] = net
        return (name == final_endpoint)
    with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
            net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding, scope='Conv2d_1a_3x3')
            if add_and_check_final('Conv2d_1a_3x3', net):
                return (net, end_points)
            net = slim.conv2d(net, 32, 3, padding=padding, scope='Conv2d_2a_3x3')
            if add_and_check_final('Conv2d_2a_3x3', net):
                return (net, end_points)
            net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
            if add_and_check_final('Conv2d_2b_3x3', net):
                return (net, end_points)
            net = slim.max_pool2d(net, 3, stride=2, padding=padding, scope='MaxPool_3a_3x3')
            if add_and_check_final('MaxPool_3a_3x3', net):
                return (net, end_points)
            net = slim.conv2d(net, 80, 1, padding=padding, scope='Conv2d_3b_1x1')
            if add_and_check_final('Conv2d_3b_1x1', net):
                return (net, end_points)
            net = slim.conv2d(net, 192, 3, padding=padding, scope='Conv2d_4a_3x3')
            if add_and_check_final('Conv2d_4a_3x3', net):
                return (net, end_points)
            net = slim.max_pool2d(net, 3, stride=2, padding=padding, scope='MaxPool_5a_3x3')
            if add_and_check_final('MaxPool_5a_3x3', net):
                return (net, end_points)
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
                with tf.variable_scope('Branch_1'):
                    tower_conv1_0 =...