def METHOD_NAME(operator):
    if "depthwise_conv2d_nhwc" not in operator.tag:
        return
    # extract tensors
    output = operator.output(0)
    padded_data = output.op.input_tensors[0]
    reshaped_kernel = output.op.input_tensors[1]
    in_dtype = padded_data.dtype
    _, padded_h, padded_w, channels = padded_data.shape
    _, kernel_h, kernel_w, _ = reshaped_kernel.shape
    suffix = "".join(random.choices(string.ascii_uppercase, k=8))
    b_ax, y_ax, x_ax, c_ax = schedule[output].op.axis
    ky_ax, kx_ax = schedule[output].op.reduce_axis
    simd_lanes = num_simd_lanes_per_word(in_dtype)
    c_ax_o, c_ax_i = schedule[output].split(c_ax, factor=simd_lanes)
    schedule[output].reorder(b_ax, c_ax_o, y_ax, x_ax, ky_ax, kx_ax, c_ax_i)
    multi_channel_convolve = intrin_multi_channel_convolve(
        in_dtype, padded_h, padded_w, channels, kernel_h, kernel_w, suffix
    )
    schedule[output].tensorize(ky_ax, multi_channel_convolve)
    schedule[output].pragma(
        b_ax,
        "import_c",
        multi_channel_convolve_impl(
            in_dtype, padded_h, padded_w, channels, kernel_h, kernel_w, suffix
        ),
    )