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

_cpp_source = r"""
#include <torch/extension.h>
torch::Tensor subtract_mish_cuda(torch::Tensor x, double s1, double s2);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/AccumulateType.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 mish_float(float x) {
    float sp = x > 20.0f ? x : log1pf(expf(x));
    return x * tanhf(sp);
}

__global__ void subtract_mish_float4(
    float* __restrict__ x,
    int64_t n,
    float s1,
    float s2
) {
    const int64_t stride = int64_t(blockDim.x) * gridDim.x;
    const int64_t start = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t vectors = n / 4;
    float4* data = reinterpret_cast<float4*>(x);

    for (int64_t i = start; i < vectors; i += stride) {
        float4 v = data[i];
        v.x = mish_float((v.x - s1) - s2);
        v.y = mish_float((v.y - s1) - s2);
        v.z = mish_float((v.z - s1) - s2);
        v.w = mish_float((v.w - s1) - s2);
        data[i] = v;
    }

    for (int64_t i = vectors * 4 + start; i < n; i += stride) {
        x[i] = mish_float((x[i] - s1) - s2);
    }
}

template <typename scalar_t>
__global__ void subtract_mish_generic(
    scalar_t* __restrict__ x,
    int64_t n,
    double subtract1,
    double subtract2
) {
    using acc_t = at::acc_type<scalar_t, true>;
    const acc_t s1 = static_cast<acc_t>(subtract1);
    const acc_t s2 = static_cast<acc_t>(subtract2);
    const int64_t stride = int64_t(blockDim.x) * gridDim.x;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < n; i += stride) {
        // Preserve rounding after each original subtraction.
        scalar_t first = static_cast<scalar_t>(static_cast<acc_t>(x[i]) - s1);
        scalar_t second = static_cast<scalar_t>(static_cast<acc_t>(first) - s2);
        acc_t v = static_cast<acc_t>(second);
        acc_t sp = v > acc_t(20) ? v : log1p(exp(v));
        x[i] = static_cast<scalar_t>(v * tanh(sp));
    }
}

torch::Tensor subtract_mish_cuda(torch::Tensor x, double s1, double s2) {
    TORCH_CHECK(x.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(
        x.is_contiguous() ||
        x.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Expected contiguous convolution output"
    );

    const c10::cuda::CUDAGuard device_guard(x.device());
    const int64_t n = x.numel();
    if (n == 0) return x;

    constexpr int threads = 256;
    const auto stream = at::cuda::getCurrentCUDAStream();

    if (x.scalar_type() == at::kFloat) {
        const int64_t work = (n + 3) / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((work + threads - 1) / threads, 4096)
        );
        subtract_mish_float4<<<blocks, threads, 0, stream>>>(
            x.data_ptr<float>(), n, static_cast<float>(s1), static_cast<float>(s2)
        );
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((n + threads - 1) / threads, 4096)
        );
        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half,
            at::ScalarType::BFloat16,
            x.scalar_type(),
            "subtract_mish_cuda",
            [&] {
                subtract_mish_generic<scalar_t><<<blocks, threads, 0, stream>>>(
                    x.data_ptr<scalar_t>(), n, s1, s2
                );
            }
        );
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_fused_ops = load_inline(
    name="fused_conv_subtract_mish",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["subtract_mish_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        subtract_value_1,
        subtract_value_2,
    ):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.subtract_value_1 = subtract_value_1
        self.subtract_value_2 = subtract_value_2

    def forward(self, x):
        x = self.conv(x)
        return _fused_ops.subtract_mish_cuda(
            x, self.subtract_value_1, self.subtract_value_2
        )
```