def METHOD_NAME(
    inputs,
    unnormalized_widths,
    unnormalized_heights,
    unnormalized_derivatives,
    inverse=False,
    left=0.0,
    right=1.0,
    bottom=0.0,
    top=1.0,
    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
    min_derivative=DEFAULT_MIN_DERIVATIVE,
):
    if torch.min(inputs) < left or torch.max(inputs) > right:
        raise ValueError("Input to a transform is not within its domain")
    num_bins = unnormalized_widths.shape[-1]
    if min_bin_width * num_bins > 1.0:
        raise ValueError("Minimal bin width too large for the number of bins")
    if min_bin_height * num_bins > 1.0:
        raise ValueError("Minimal bin height too large for the number of bins")
    widths = F.softmax(unnormalized_widths, dim=-1)
    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
    cumwidths = torch.cumsum(widths, dim=-1)
    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
    cumwidths = (right - left) * cumwidths + left
    cumwidths[..., 0] = left
    cumwidths[..., -1] = right
    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
    heights = F.softmax(unnormalized_heights, dim=-1)
    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
    cumheights = torch.cumsum(heights, dim=-1)
    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
    cumheights = (top - bottom) * cumheights + bottom
    cumheights[..., 0] = bottom
    cumheights[..., -1] = top
    heights = cumheights[..., 1:] - cumheights[..., :-1]
    if inverse:
        bin_idx = searchsorted(cumheights, inputs)[..., None]
    else:
        bin_idx = searchsorted(cumwidths, inputs)[..., None]
    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
    delta = heights / widths
    input_delta = delta.gather(-1, bin_idx)[..., 0]
    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
    input_heights = heights.gather(-1, bin_idx)[..., 0]
    if inverse:
        a = (inputs - input_cumheights) * (
            input_derivatives + input_derivatives_plus_one - 2 * input_delta
        ) + input_heights * (input_delta - input_derivatives)
        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
            input_derivatives + input_derivatives_plus_one - 2 * input_delta
        )
        c = -input_delta * (inputs - input_cumheights)
        discriminant = b.pow(2) - 4 * a * c
        assert (discriminant >= 0).all()
        root = (2 * c) / (-b - torch.sqrt(discriminant))
        outputs = root * input_bin_widths + input_cumwidths
        theta_one_minus_theta = root * (1 - root)
        denominator = input_delta + (
            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
            * theta_one_minus_theta
        )
        derivative_numerator = input_delta.pow(2) * (
            input_derivatives_plus_one * root.pow(2)
            + 2 * input_delta * theta_one_minus_theta
            + input_derivatives * (1 - root).pow(2)
        )
        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
        return outputs, -logabsdet
    else:
        theta = (inputs - input_cumwidths) / input_bin_widths
        theta_one_minus_theta = theta * (1 - theta)
        numerator = input_heights * (
            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
        )
        denominator = input_delta + (
            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
            * theta_one_minus_theta
        )
        outputs = input_cumheights + numerator / denominator
        derivative_numerator = input_delta.pow(2) * (
            input_derivatives_plus_one * theta.pow(2)
            + 2 * input_delta * theta_one_minus_theta
            + input_derivatives * (1 - theta).pow(2)
        )
        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
        return outputs, logabsdet