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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 CaffeBilinearUpSample(x, shape):
 """ Deterministic bilinearly-upsample the input images. It is implemented by deconvolution with "BilinearFiller" in Caffe. It is aimed to mimic caffe behavior. Args: x (tf.Tensor):
 a NCHW tensor shape (int):
 the upsample factor Returns: tf.Tensor: a NCHW tensor. """

    inp_shape = x.shape.as_list()
    ch = inp_shape[1]
    assert ch == 1, "This layer only works for channel=1"
    # for a version that supports >1 channels, see:
    # https://github.com/tensorpack/tensorpack/issues/1040#issuecomment-452798180

    shape = int(shape)
    filter_shape = 2 * shape

    def bilinear_conv_filler(s):
        """
        s: width, height of the conv filter
        https://github.com/BVLC/caffe/blob/99bd99795dcdf0b1d3086a8d67ab1782a8a08383/include/caffe/filler.hpp#L219-L268
        """
        f = np.ceil(float(s) / 2)
        c = float(2 * f - 1 - f % 2) / (2 * f)
        ret = np.zeros((s, s), dtype='float32')
        for x in range(s):
            for y in range(s):
                ret[x, y] = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
        return ret

    w = bilinear_conv_filler(filter_shape)
    w = np.repeat(w, ch * ch).reshape((filter_shape, filter_shape, ch, ch))

    weight_var = tf.constant(w, tf.float32,
                             shape=(filter_shape, filter_shape, ch, ch),
                             name='bilinear_upsample_filter')
    x = tf.pad(x, [[0, 0], [0, 0], [shape - 1, shape - 1], [shape - 1, shape - 1]], mode='SYMMETRIC')
    out_shape = tf.shape(x) * tf.constant([1, 1, shape, shape], tf.int32)
    deconv = tf.nn.conv2d_transpose(x, weight_var, out_shape,
                                    [1, 1, shape, shape], 'SAME', data_format='NCHW')
    edge = shape * (shape - 1)
    deconv = deconv[:, :, edge:-edge, edge:-edge]

    if inp_shape[2]:
        inp_shape[2] *= shape
    if inp_shape[3]:
        inp_shape[3] *= shape
    deconv.set_shape(inp_shape)
    return deconv