```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 subtract1, double subtract2);
"""

_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 subtract_mish(
    float x, float subtract1, float subtract2
) {
    x = x - subtract1;
    x = x - subtract2;
    const float softplus = x > 20.0f ? x : log1pf(expf(x));
    return x * tanhf(softplus);
}

__global__ void subtract_mish_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t count,
    float subtract1,
    float subtract2
) {
    const int64_t thread =
        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 vectors = count / 4;

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

    for (int64_t i = thread; i < vectors; i += stride) {
        const float4 v = input4[i];
        float4 result;
        result.x = subtract_mish(v.x, subtract1, subtract2);
        result.y = subtract_mish(v.y, subtract1, subtract2);
        result.z = subtract_mish(v.z, subtract1, subtract2);
        result.w = subtract_mish(v.w, subtract1, subtract2);
        output4[i] = result;
    }

    const int64_t tail = vectors * 4 + thread;
    if (tail < count) {
        output[tail] = subtract_mish(input[tail], subtract1, subtract2);
    }
}

torch::Tensor subtract_mish_cuda(
    torch::Tensor x, double subtract1, double subtract2
) {
    TORCH_CHECK(x.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "Expected float32");
    TORCH_CHECK(x.is_contiguous(), "Expected contiguous input");

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

    if (count > 0) {
        constexpr int threads = 256;
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads * 4 - 1) / (threads * 4), 4096)
        );

        subtract_mish_kernel<<<
            blocks, threads, 0, at::cuda::getCurrentCUDAStream()
        >>>(
            x.data_ptr<float>(),
            output.data_ptr<float>(),
            count,
            static_cast<float>(subtract1),
            static_cast<float>(subtract2)
        );
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    return output;
}
"""

_fused = 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 _SubtractMish(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, subtract1, subtract2):
        if ctx.needs_input_grad[0]:
            ctx.save_for_backward(x)
            ctx.subtract1 = subtract1
            ctx.subtract2 = subtract2
        return _fused.subtract_mish_cuda(x, subtract1, subtract2)

    @staticmethod
    def backward(ctx, grad_output):
        (x,) = ctx.saved_tensors
        z = (x - ctx.subtract1) - ctx.subtract2
        t = torch.tanh(F.softplus(z))
        derivative = t + z * torch.sigmoid(z) * (1.0 - t * t)
        return grad_output * derivative, None, None


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 x.is_contiguous():
            return _SubtractMish.apply(
                x, self.subtract_value_1, self.subtract_value_2
            )
        return F.mish(
            (x - self.subtract_value_1) - self.subtract_value_2
        )
```