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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def init_conv_weight(layer):
    '''initilize conv layer weight.
    '''
    n_filters = layer.filters
    filter_shape = (layer.kernel_size,) * get_n_dim(layer)
    weight = np.zeros((n_filters, n_filters) + filter_shape)

    center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
    for i in range(n_filters):
        filter_weight = np.zeros((n_filters,) + filter_shape)
        index = (i,) + center
        filter_weight[index] = 1
        weight[i, ...] = filter_weight
    bias = np.zeros(n_filters)

    layer.set_weights(
        (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
    )