human
Complete the below
def ResNet_FullPre_Wide(input_var=None, n=6, k=4):

    ''''Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.
    Tweaked to be consistent with \'Identity Mappings in Deep Residual Networks\', Kaiming He et al. 2016 (https://arxiv.org/abs/1603.05027)
    And \'Wide Residual Networks\', Sergey Zagoruyko, Nikos Komodakis 2016 (http://arxiv.org/pdf/1605.07146v1.pdf)
    Depth = 6n + 2'
    '''
assistant
#Here is the Python code you requested.
    n_filters = {0: 16, 1: (16 * k), 2: (32 * k), 3: (64 * k)}
    def residual_block(l, increase_dim=False, projection=True, first=False, filters=16):
        if increase_dim:
            first_stride = (2, 2)
        else:
            first_stride = (1, 1)
        if first:
            bn_pre_relu = l
        else:
            bn_pre_conv = BatchNormLayer(l)
            bn_pre_relu = NonlinearityLayer(bn_pre_conv, rectify)
        conv_1 = batch_norm(ConvLayer(bn_pre_relu, num_filters=filters, filter_size=(3, 3), stride=first_stride, nonlinearity=rectify, pad='same', W=he_norm))
        dropout = DropoutLayer(conv_1, p=0.3)
        conv_2 = ConvLayer(dropout, num_filters=filters, filter_size=(3, 3), stride=(1, 1), nonlinearity=None, pad='same', W=he_norm)
        if increase_dim:
            projection = ConvLayer(l, num_filters=filters, filter_size=(1, 1), stride=(2, 2), nonlinearity=None, pad='same', b=None)
            block = ElemwiseSumLayer([conv_2, projection])
        elif first:
            projection = ConvLayer(l, num_filters=filters, filter_size=(1, 1), stride=(1, 1), nonlinearity=None, pad='same', b=None)
            block = ElemwiseSumLayer([conv_2, projection])
        else:
            block = ElemwiseSumLayer([conv_2, l])
        return block
    l_in = InputLayer(shape=(None, 3, PIXELS, PIXELS), input_var=input_var)
    l = batch_norm(ConvLayer(l_in, num_filters=n_filters[0], filter_size=(3, 3), stride=(1, 1), nonlinearity=rectify, pad='same', W=he_norm))
    l = residual_block(l, first=True, filters=n_filters[1])
    for _ in range(1, n):
        l = residual_block(l, filters=n_filters[1])
    l = residual_block(l, increase_dim=True, filters=n_filters[2])
    for _ in range(1, (n + 2)):
        l = residual_block(l, filters=n_filters[2])
    l = residual_block(l, increase_dim=True, filters=n_filters[3])
    for _ in range(1, (n + 2)):
        l = residual_block(l, filters=n_filters[3])
    bn_post_conv = BatchNormLayer(l)
    bn_post_relu = NonlinearityLayer(bn_post_conv, rectify)
   ...