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

_cpp_source = """
#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>

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

__global__ void fused_float4_kernel(
    const float* __restrict__ x,
    float* __restrict__ out,
    int64_t n,
    float add_value,
    float scale) {
    int64_t index =
        (static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x) * 4;
    int64_t step = static_cast<int64_t>(gridDim.x) * blockDim.x * 4;

    for (; index < n; index += step) {
        if (index + 3 < n) {
            float4 v = *reinterpret_cast<const float4*>(x + index);
            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);
            *reinterpret_cast<float4*>(out + index) = v;
        } else {
            for (int64_t j = index; j < n; ++j) {
                out[j] = activate(x[j], 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 a contiguous tensor");

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

    constexpr int threads = 256;
    int64_t requested_blocks = (n + threads * 4 - 1) / (threads * 4);
    int blocks = static_cast<int>(
        requested_blocks < 65535 ? requested_blocks : 65535);

    fused_float4_kernel<<<
        blocks, threads, 0, at::cuda::getCurrentCUDAStream()
    >>>(
        x.data_ptr<float>(),
        out.data_ptr<float>(),
        n,
        static_cast<float>(add_value),
        static_cast<float>(scale));

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return out;
}
"""

_fused_ops = load_inline(
    name="fused_mish_add_hardtanh_scale",
    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)
        if x.is_cuda and x.dtype == torch.float32:
            return _fused_ops.fused_mish_hardtanh_cuda(
                x, self.add_value, self.scale
            )
        return F.hardtanh(
            F.mish(x) + self.add_value, min_val=-1, max_val=1
        ) * self.scale
```