```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) {
    const float softplus = x > 20.0f ? x : log1pf(expf(x));
    float y = x * tanhf(softplus);
    y += add_value;
    y = y < -1.0f ? -1.0f : y;
    y = y >  1.0f ?  1.0f : y;
    return y * scale;
}

__global__ void fused_activation_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t count,
    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 vector_count = count / 4;

    const float4* input4 = reinterpret_cast<const float4*>(input);
    float4* output4 = reinterpret_cast<float4*>(output);

    for (int64_t i = tid; i < vector_count; i += step) {
        const float4 v = input4[i];
        float4 result;
        result.x = activate(v.x, add_value, scale);
        result.y = activate(v.y, add_value, scale);
        result.z = activate(v.z, add_value, scale);
        result.w = activate(v.w, add_value, scale);
        output4[i] = result;
    }

    for (int64_t i = vector_count * 4 + tid; i < count; i += step) {
        output[i] = activate(input[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");
    TORCH_CHECK(
        x.is_contiguous() ||
        x.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Expected contiguous or channels-last storage");

    const c10::cuda::CUDAGuard device_guard(x.device());
    auto output = torch::empty_like(x);
    const int64_t count = x.numel();

    if (count == 0) {
        return output;
    }

    constexpr int threads = 256;
    const int64_t work_items = (count + 3) / 4;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work_items + threads - 1) / threads, 4096));

    fused_activation_kernel<<<
        blocks, threads, 0, at::cuda::getCurrentCUDAStream()
    >>>(
        x.data_ptr<float>(),
        output.data_ptr<float>(),
        count,
        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", "--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.add_value = add_value
        self.scale = scale

    def forward(self, x):
        x = self.conv_transpose(x)
        if x.is_cuda and x.dtype == torch.float32:
            if not (
                x.is_contiguous()
                or x.is_contiguous(memory_format=torch.channels_last)
            ):
                x = x.contiguous()
            return _fused_ops.fused_mish_hardtanh_cuda(
                x, float(self.add_value), float(self.scale)
            )

        return F.hardtanh(
            F.mish(x) + self.add_value, min_val=-1, max_val=1
        ) * self.scale
```