def METHOD_NAME(cfg, data, kernel, stride, padding, out_dtype, output_padding):
    """Transposed 1D convolution ncw forward operator.
    Parameters
    ----------
    cfg: ConfigEntity
        The config for this template
    Input : tvm.te.Tensor
        3-D with shape [batch, in_channel, inp_width]
    Filter : tvm.te.Tensor
        3-D with shape [in_channel, num_filter, kernel_size]
    stride : tuple of one int
        The spatial stride along width
    padding : int, tuple, or string
        int: padding size
        tuple of 2 ints: (pad_left, pad_right) for left and right padding
        string: ['VALID', 'SAME']
    out_dtype: str
        The output type. This is used in mixed precision
    output_padding : ints
        Used to disambiguate the output shape.
    Returns
    -------
    Output : tvm.te.Tensor
    u    3-D with shape [batch, out_channel, out_width]
    """
    if isinstance(stride, (tuple, list)):
        stride = stride[0]
    if isinstance(output_padding, (tuple, list)):
        output_padding = output_padding[0]
    assert output_padding < stride
    cfg.stride = stride
    cfg.output_padding = output_padding
    batch, inp_channels, inp_width = get_const_tuple(data.shape)
    _, out_channels, kernel_size = get_const_tuple(kernel.shape)
    pad_left, pad_right = nn.get_pad_tuple1d(padding, kernel_size)
    out_width = (inp_width - 1) * stride + kernel_size - pad_left - pad_right + output_padding
    pad_left = kernel_size - 1 - pad_left
    pad_right = kernel_size - 1 - pad_right + output_padding
    padded_width = pad_left + inp_width + pad_right
    padded_data = te.compute(
        (batch, inp_channels, padded_width),
        lambda n, c, x: tvm.tir.if_then_else(
            tvm.tir.all(x >= pad_left, x < pad_left + inp_width),
            data[n, c, x - pad_left],
            tvm.tir.const(0.0, "float32"),
        ),
        name="data_pad",
    )
    padded_kernel = te.compute(
        (inp_channels, out_channels, kernel_size + stride - 1),
        lambda ci, co, k: tvm.tir.if_then_else(
            tvm.tir.all(k < kernel_size),
            kernel[ci, co, kernel_size - k - 1],
            tvm.tir.const(0.0, "float32"),
        ),
        name="kernel_pad",
    )
    ci = te.reduce_axis((0, inp_channels), name="ci")
    k = te.reduce_axis((0, tvm.tir.indexdiv(kernel_size + stride - 1, stride)), name="k")
    border = pad_left * (stride - 1)
    # Skip multiplication by 0 values in the input data inserted when stride is greater then 1.
    # During multiplication of kernel by padded data:
    #  Kernel indices are: 0, 1 * stride, 2 * stride, ..., ceil(kernel_size / stride) plus
    #  data offset mod stride
    data_out = te.compute(
        (batch, out_channels, out_width),
        lambda b, co, w: te.sum(
            padded_data[b, ci, tvm.tir.indexdiv(border + w + stride - 1, stride) + k].astype(
                out_dtype
            )
            * padded_kernel[
                ci, co, k * stride + tvm.tir.indexmod(stride - w - border, stride)
            ].astype(out_dtype),
            axis=[ci, k],
        ),
        tag="conv1d_transpose_ncw",
    )
    return data_out