```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline


_cpp_source = r"""
#include <torch/extension.h>

torch::Tensor fused_bias_mish_cuda(
    torch::Tensor x, torch::Tensor bias, double add_value, double scale);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cstdint>

__device__ __forceinline__ float activate(
    float x, float bias, float add_value, float scale) {
    x = x + bias;

    float mish;
    if (x > 20.0f) {
        mish = x;
    } else {
        const float e = expf(x);
        const float numerator = e * (e + 2.0f);
        mish = x * (numerator / (numerator + 2.0f));
    }

    float y = mish + add_value;
    y = y < -1.0f ? -1.0f : y;
    y = y >  1.0f ?  1.0f : y;
    return y * scale;
}

template<int FixedChannels>
__global__ void fused_vector_kernel(
    const float4* __restrict__ input,
    const float4* __restrict__ bias,
    float4* __restrict__ output,
    int64_t vector_count,
    int channels,
    float add_value,
    float scale) {

    const int channels4 =
        (FixedChannels > 0 ? FixedChannels : channels) / 4;
    const int64_t first =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t step =
        static_cast<int64_t>(gridDim.x) * blockDim.x;

    for (int64_t i = first; i < vector_count; i += step) {
        const float4 v = input[i];
        const float4 b = bias[i % channels4];
        float4 result;
        result.x = activate(v.x, b.x, add_value, scale);
        result.y = activate(v.y, b.y, add_value, scale);
        result.z = activate(v.z, b.z, add_value, scale);
        result.w = activate(v.w, b.w, add_value, scale);
        output[i] = result;
    }
}

__global__ void fused_scalar_kernel(
    const float* __restrict__ input,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int64_t count,
    int channels,
    float add_value,
    float scale) {

    const int64_t first =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t step =
        static_cast<int64_t>(gridDim.x) * blockDim.x;

    for (int64_t i = first; i < count; i += step) {
        output[i] = activate(
            input[i], bias[i % channels], add_value, scale);
    }
}

torch::Tensor fused_bias_mish_cuda(
    torch::Tensor x,
    torch::Tensor bias,
    double add_value,
    double scale) {

    TORCH_CHECK(x.is_cuda() && bias.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(
        x.scalar_type() == at::kFloat &&
        bias.scalar_type() == at::kFloat,
        "Expected float32 tensors");
    TORCH_CHECK(x.device() == bias.device(), "Device mismatch");
    TORCH_CHECK(
        x.dim() == 4 &&
        x.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Expected channels-last input");
    TORCH_CHECK(
        bias.is_contiguous() && bias.numel() == x.size(1),
        "Invalid bias");

    const c10::cuda::CUDAGuard guard(x.device());
    auto output = torch::empty_like(x);
    const int64_t count = x.numel();
    if (count == 0) {
        return output;
    }

    const int channels = static_cast<int>(x.size(1));
    constexpr int threads = 256;
    const bool vectorized = channels % 4 == 0;
    const int64_t work = vectorized ? count / 4 : count;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work + threads - 1) / threads, 4096));
    const auto stream = at::cuda::getCurrentCUDAStream();

    if (vectorized) {
        const auto* input4 =
            reinterpret_cast<const float4*>(x.data_ptr<float>());
        const auto* bias4 =
            reinterpret_cast<const float4*>(bias.data_ptr<float>());
        auto* output4 =
            reinterpret_cast<float4*>(output.data_ptr<float>());

        if (channels == 64) {
            fused_vector_kernel<64><<<blocks, threads, 0, stream>>>(
                input4, bias4, output4, work, channels,
                static_cast<float>(add_value), static_cast<float>(scale));
        } else {
            fused_vector_kernel<0><<<blocks, threads, 0, stream>>>(
                input4, bias4, output4, work, channels,
                static_cast<float>(add_value), static_cast<float>(scale));
        }
    } else {
        fused_scalar_kernel<<<blocks, threads, 0, stream>>>(
            x.data_ptr<float>(), bias.data_ptr<float>(),
            output.data_ptr<float>(), count, channels,
            static_cast<float>(add_value), static_cast<float>(scale));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_fused_ops = load_inline(
    name="channels_last_bias_mish_hardtanh_scale_v2",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fused_bias_mish_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3", "--fmad=false"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        add_value,
        scale,
    ):
        super().__init__()
        self.conv_transpose = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            output_padding,
        )
        self.conv_transpose.to(memory_format=torch.channels_last)
        self.add_value = add_value
        self.scale = scale

    def forward(self, x):
        conv = self.conv_transpose
        if (
            x.is_cuda
            and x.dtype == torch.float32
            and conv.weight.dtype == torch.float32
            and not torch.is_autocast_enabled()
        ):
            x = x.contiguous(memory_format=torch.channels_last)
            x = F.conv_transpose2d(
                x,
                conv.weight,
                bias=None,
                stride=conv.stride,
                padding=conv.padding,
                output_padding=conv.output_padding,
                groups=conv.groups,
                dilation=conv.dilation,
            )
            x = x.contiguous(memory_format=torch.channels_last)
            return _fused_ops.fused_bias_mish_cuda(
                x, conv.bias, float(self.add_value), float(self.scale)
            )

        x = conv(x)
        return F.hardtanh(
            F.mish(x) + self.add_value, min_val=-1.0, max_val=1.0
        ) * self.scale
```