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


_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 subtract_1, float subtract_2
) {
    x = __fsub_rn(x, subtract_1);
    x = __fsub_rn(x, subtract_2);
    const float softplus = x > 20.0f ? x : log1pf(expf(x));
    return x * tanhf(softplus);
}

__global__ void subtract_mish_kernel(
    float* __restrict__ data,
    int64_t count,
    float subtract_1,
    float subtract_2
) {
    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 vector_count = count / 4;

    float4* vectors = reinterpret_cast<float4*>(data);
    for (int64_t i = tid; i < vector_count; i += stride) {
        float4 v = vectors[i];
        v.x = subtract_mish(v.x, subtract_1, subtract_2);
        v.y = subtract_mish(v.y, subtract_1, subtract_2);
        v.z = subtract_mish(v.z, subtract_1, subtract_2);
        v.w = subtract_mish(v.w, subtract_1, subtract_2);
        vectors[i] = v;
    }

    const int64_t tail = vector_count * 4 + tid;
    if (tail < count) {
        data[tail] = subtract_mish(
            data[tail], subtract_1, subtract_2
        );
    }
}

torch::Tensor subtract_mish_cuda(
    torch::Tensor input,
    double subtract_1,
    double subtract_2
) {
    TORCH_CHECK(input.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(input.scalar_type() == at::kFloat, "Expected float32");
    TORCH_CHECK(
        input.is_contiguous() ||
        input.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Expected a contiguous tensor"
    );

    const c10::cuda::CUDAGuard device_guard(input.device());
    const int64_t count = input.numel();
    if (count == 0) {
        return input;
    }

    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, 65535)
    );

    subtract_mish_kernel<<<
        blocks, threads, 0,
        at::cuda::getCurrentCUDAStream(input.get_device())
    >>>(
        input.data_ptr<float>(),
        count,
        static_cast<float>(subtract_1),
        static_cast<float>(subtract_2)
    );
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return input;
}
"""

_ops = load_inline(
    name="conv_subtract_subtract_mish_fused_v1",
    cpp_sources=r"""
        torch::Tensor subtract_mish_cuda(
            torch::Tensor input,
            double subtract_1,
            double subtract_2
        );
    """,
    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):
        y = self.conv(x)

        if (
            y.is_cuda
            and y.dtype == torch.float32
            and not (torch.is_grad_enabled() and y.requires_grad)
            and (
                y.is_contiguous()
                or y.is_contiguous(memory_format=torch.channels_last)
            )
        ):
            return _ops.subtract_mish_cuda(
                y,
                self.subtract_value_1,
                self.subtract_value_2,
            )

        return F.mish(
            (y - self.subtract_value_1) - self.subtract_value_2
        )
```