```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 subtract_mish_cuda(torch::Tensor x, double a, double b);
"""

_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>

__device__ __forceinline__ float subtract_mish(
    float x, float a, float b
) {
    float z = __fsub_rn(__fsub_rn(x, a), b);
    float softplus = z > 20.0f ? z : log1pf(expf(z));
    return z * tanhf(softplus);
}

__global__ void subtract_mish_kernel(
    float* __restrict__ x,
    int64_t n,
    float a,
    float b
) {
    const int64_t tid =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride =
        static_cast<int64_t>(blockDim.x) * gridDim.x;
    const int64_t n4 = n / 4;
    float4* vectors = reinterpret_cast<float4*>(x);

    for (int64_t i = tid; i < n4; i += stride) {
        float4 v = vectors[i];
        v.x = subtract_mish(v.x, a, b);
        v.y = subtract_mish(v.y, a, b);
        v.z = subtract_mish(v.z, a, b);
        v.w = subtract_mish(v.w, a, b);
        vectors[i] = v;
    }

    const int64_t tail = n4 * 4 + tid;
    if (tail < n) {
        x[tail] = subtract_mish(x[tail], a, b);
    }
}

torch::Tensor subtract_mish_cuda(torch::Tensor x, double a, double b) {
    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 dense contiguous storage"
    );

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

    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)
    );

    subtract_mish_kernel<<<
        blocks, threads, 0, at::cuda::getCurrentCUDAStream()
    >>>(
        x.data_ptr<float>(), n,
        static_cast<float>(a), static_cast<float>(b)
    );
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_fused_ops = load_inline(
    name="conv_subtract_mish_fused_v1",
    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)
        if (
            x.is_cuda
            and x.dtype == torch.float32
            and not (torch.is_grad_enabled() and x.requires_grad)
        ):
            return _fused_ops.subtract_mish_cuda(
                x, self.subtract_value_1, self.subtract_value_2
            )
        return F.mish(
            (x - self.subtract_value_1) - self.subtract_value_2
        )
```