N-D Depthwise Convolution with a bias term.

    Reference:

    - F. Chollet: Chollet, Francois. "Xception: Deep Learning with Depthwise Separable Convolutions. https://arxiv.org/abs/1610.02357

    Args:
        inp (~nnabla.Variable): N-D array.
        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.
        multiplier (:obj:`int`): Number of output feature maps per input feature map.
        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`. 
        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`: N-D array. See :obj:`~nnabla.functions.depthwise_convolution` for the output shape.

def depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None,
                          multiplier=1, w_init=None, b_init=None, base_axis=1,
                          fix_parameters=False, rng=None, with_bias=True):
    """
    N-D Depthwise Convolution with a bias term.

    Reference:

    - F. Chollet: Chollet, Francois. "Xception: Deep Learning with Depthwise Separable Convolutions. https://arxiv.org/abs/1610.02357

    Args:
        inp (~nnabla.Variable): N-D array.
        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.
        multiplier (:obj:`int`): Number of output feature maps per input feature map.
        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`. 
        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`: N-D array. See :obj:`~nnabla.functions.depthwise_convolution` for the output shape.

    """
    if w_init is None:
        w_init = UniformInitializer(
            calc_uniform_lim_glorot(
                inp.shape[base_axis] * multiplier,
                inp.shape[base_axis],
                tuple(kernel)),
            rng=rng)
    if with_bias and b_init is None:
        b_init = ConstantInitializer()
    w = get_parameter_or_create(
        "W", (inp.shape[base_axis] * multiplier,) + tuple(kernel),
        w_init, True, not fix_parameters)
    b = None
    if with_bias:
        b = get_parameter_or_create(
            "b", (inp.shape[base_axis] * multiplier,),
            b_init, True, not fix_parameters)
    return F.depthwise_convolution(inp, w, b, base_axis, pad, stride, dilation,
                                   multiplier)