def METHOD_NAME(a_np, w_np, stride, padding):
    """Convolution operator in NCHW layout.
    Parameters
    ----------
    a_np : numpy.ndarray
        4-D with shape [batch, in_channel, in_height, in_width]
    w_np : numpy.ndarray
        4-D with shape [num_filter, in_channel, filter_height, filter_width]
    stride : int or a list/tuple of two ints
        Stride size, or [stride_height, stride_width]
    padding : int or str or a list/tuple of 2 or 4 ints
        Padding size, or ['VALID', 'SAME'], or
        [pad_height, pad_width] for 2 ints, or
        [pad_top, pad_left, pad_bottom, pad_right] for 2 ints
    Returns
    -------
    b_np : np.ndarray
        4-D with shape [batch, out_channel, out_height, out_width]
    """
    batch, in_channel, in_height, in_width = a_np.shape
    num_filter, _, kernel_h, kernel_w = w_np.shape
    if isinstance(stride, int):
        stride_h = stride_w = stride
    else:
        stride_h, stride_w = stride
    pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel_h, kernel_w))
    pad_h = pad_top + pad_bottom
    pad_w = pad_left + pad_right
    # compute the output shape
    out_channel = num_filter
    out_height = (in_height - kernel_h + pad_h) // stride_h + 1
    out_width = (in_width - kernel_w + pad_w) // stride_w + 1
    b_np = np.zeros((batch, out_channel, out_height, out_width), dtype=a_np.dtype)
    # computation
    for n in range(batch):
        for f in range(out_channel):
            for c in range(in_channel):
                if pad_h > 0 or pad_w > 0:
                    apad = np.zeros((in_height + pad_h, in_width + pad_w), dtype=a_np.dtype)
                    apad[pad_top : pad_top + in_height, pad_left : pad_left + in_width] = a_np[n, c]
                else:
                    apad = a_np[n, c]
                out = _conv2d_hw(apad, w_np[f, c])
                b_np[n, f] += out[::stride_h, ::stride_w]
    return b_np