```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 = (x - subtract_1) - 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 size,
    float subtract_1,
    float subtract_2
) {
    const int64_t index =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride =
        static_cast<int64_t>(blockDim.x) * gridDim.x;

    float4* vectors = reinterpret_cast<float4*>(data);
    const int64_t vector_count = size / 4;

    for (int64_t i = index; i < vector_count; i += stride) {
        float4 value = vectors[i];
        value.x = subtract_mish(value.x, subtract_1, subtract_2);
        value.y = subtract_mish(value.y, subtract_1, subtract_2);
        value.z = subtract_mish(value.z, subtract_1, subtract_2);
        value.w = subtract_mish(value.w, subtract_1, subtract_2);
        vectors[i] = value;
    }

    const int64_t tail_index = vector_count * 4 + index;
    if (tail_index < size) {
        data[tail_index] =
            subtract_mish(data[tail_index], 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 a float32 tensor"
    );
    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 size = input.numel();
    if (size == 0) {
        return input;
    }

    constexpr int threads = 256;
    const int64_t work_items = (size + 3) / 4;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work_items + threads - 1) / threads, 4096)
    );

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

_fused_ops = load_inline(
    name="conv_subtract_mish_fused_cuda",
    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):
        x = self.conv(x)

        # Preserve autograd support when gradients are required.
        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or (torch.is_grad_enabled() and x.requires_grad)
        ):
            return F.mish(
                (x - self.subtract_value_1) - self.subtract_value_2
            )

        return _fused_ops.subtract_mish_cuda_(
            x, self.subtract_value_1, self.subtract_value_2
        )
```