```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_mish_hardtanh_cuda(
    torch::Tensor x, 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 add_value, float scale)
{
    float mish = x > 20.0f
        ? x
        : x * tanhf(log1pf(expf(x)));
    float y = mish + add_value;
    y = y < -1.0f ? -1.0f : y;
    y = y >  1.0f ?  1.0f : y;
    return y * scale;
}

__global__ void fused_kernel(
    const float* __restrict__ x,
    float* __restrict__ out,
    int64_t n,
    float add_value,
    float scale)
{
    const int64_t tid =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t step =
        static_cast<int64_t>(gridDim.x) * blockDim.x;
    const int64_t n4 = n / 4;

    const float4* x4 = reinterpret_cast<const float4*>(x);
    float4* out4 = reinterpret_cast<float4*>(out);

    for (int64_t i = tid; i < n4; i += step) {
        float4 v = x4[i];
        v.x = activate(v.x, add_value, scale);
        v.y = activate(v.y, add_value, scale);
        v.z = activate(v.z, add_value, scale);
        v.w = activate(v.w, add_value, scale);
        out4[i] = v;
    }

    for (int64_t i = n4 * 4 + tid; i < n; i += step) {
        out[i] = activate(x[i], add_value, scale);
    }
}

torch::Tensor fused_mish_hardtanh_cuda(
    torch::Tensor x, double add_value, double scale)
{
    TORCH_CHECK(x.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "Expected float32");

    const c10::cuda::CUDAGuard guard(x.device());
    auto input = x.contiguous();
    auto output = torch::empty_like(input);
    const int64_t n = input.numel();

    if (n == 0)
        return output;

    constexpr int threads = 256;
    const int64_t work = (n + 3) / 4;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work + threads - 1) / threads, 65535));

    fused_kernel<<<blocks, threads, 0,
        at::cuda::getCurrentCUDAStream()>>>(
            input.data_ptr<float>(),
            output.data_ptr<float>(),
            n,
            static_cast<float>(add_value),
            static_cast<float>(scale));

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_fused_ops = load_inline(
    name="fused_mish_add_hardtanh_scale_v1",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fused_mish_hardtanh_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    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.add_value = add_value
        self.scale = scale

    def forward(self, x):
        x = self.conv_transpose(x)

        # Preserve autograd and other dtype/device behavior when needed.
        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or (torch.is_grad_enabled() and x.requires_grad)
        ):
            return F.hardtanh(
                F.mish(x) + self.add_value, min_val=-1, max_val=1
            ) * self.scale

        return _fused_ops.fused_mish_hardtanh_cuda(
            x, float(self.add_value), float(self.scale)
        )
```