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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 binary_connect_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, quantize_zero_to=1.0, w_init=None, wb_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True):
 """Binary Connect Convolution, multiplier-less inner-product. Binary Connect Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows: .. math:: y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m, i, j}) x_{m, a + i, b + j}. Therefore :math:`sign(w_i)` is either :math:`1` or :math:`-1` and the inner product simplifies to addition. This function should be used together with BatchNormalization. References: M. Courbariaux, Y. Bengio, and J.-P. David. "BinaryConnect: Training Deep Neural Networks with binary weights during propagations." Advances in Neural Information Processing Systems. 2015. .. note:: 1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (`weight`) and not the binarized weights (`binary_weight`) 2) The weights and the binary weights become synced only after :func:`~nnabla._variable.Variable.forward` is called, and not after a call to :func:`~nnabla._variable.Variable.backward`. To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the float weights and the binary weights will not be in sync. 3) Quantized values are stored as floating point number for `binary_weight`, since this function is only for simulation purposes. Args: inp (~nnabla.Variable):
 N-D array. outmaps (int):
 Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16. kernel (:obj:`tuple` of :obj:`int`):
 Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5). pad (:obj:`tuple` of :obj:`int`):
 Padding sizes for dimensions. stride (:obj:`tuple` of :obj:`int`):
 Stride sizes for dimensions. dilation (:obj:`tuple` of :obj:`int`):
 Dilation sizes for dimensions. group (int):
 Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction. quantize_zero_to (float):
 Input value at zero is quantized to this value. w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
 Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`. wb_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
 Initializer for binary weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`. b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
 Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`. base_axis (int):
 Dimensions up to `base_axis` are treated as the sample dimensions. fix_parameters (bool):
 When set to `True`, the weights and biases will not be updated. rng (numpy.random.RandomState):
 Random generator for Initializer. with_bias (bool):
 Specify whether to include the bias term. Returns: :class:`~nnabla.Variable` """

    if w_init is None:
        w_init = UniformInitializer(
            calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
    if wb_init is None:
        wb_init = UniformInitializer(
            calc_uniform_lim_glorot(inp.shape[base_axis], outmaps, tuple(kernel)), rng=rng)
    if b_init is None:
        b_init = ConstantInitializer()
    w = get_parameter_or_create(
        "W", (outmaps, inp.shape[base_axis]) + tuple(kernel),
        w_init, True, not fix_parameters)
    wb = get_parameter_or_create(
        "Wb", (outmaps, inp.shape[base_axis]) + tuple(kernel),
        wb_init, False)
    b = None
    if with_bias:
        b = get_parameter_or_create(
            "b", (outmaps,), b_init, True, not fix_parameters)
    return F.binary_connect_convolution(inp, w, wb, b, base_axis, pad, stride, dilation, group, quantize_zero_to)