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

_cpp_source = r"""
#include <torch/extension.h>
torch::Tensor bias_subtract_mish_cuda(
    torch::Tensor x, torch::Tensor bias, 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 fast_mish(float x) {
    if (x > 20.0f) return x;
    float e = __expf(x);
    float q = e * (e + 2.0f);
    return x * __fdividef(q, q + 2.0f);
}

template<bool ChannelsLast, bool SharedBias>
__global__ void fused_float4(
    float* __restrict__ x,
    const float* __restrict__ bias,
    int64_t n,
    int64_t spatial,
    int channels,
    float s1,
    float s2
) {
    const int64_t start =
        int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride = int64_t(blockDim.x) * gridDim.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];
        float* values = reinterpret_cast<float*>(&v);
        float shared_bias = 0.0f;
        if (SharedBias)
            shared_bias = bias[((i * 4) / spatial) % channels];

        #pragma unroll
        for (int j = 0; j < 4; ++j) {
            int64_t index = i * 4 + j;
            float b;
            if (SharedBias) {
                b = shared_bias;
            } else {
                int c = ChannelsLast
                    ? index % channels
                    : (index / spatial) % channels;
                b = bias[c];
            }
            float z = values[j] + b;
            z = z - s1;
            z = z - s2;
            values[j] = fast_mish(z);
        }
        data[i] = v;
    }

    for (int64_t i = vectors * 4 + start; i < n; i += stride) {
        int c = ChannelsLast
            ? i % channels
            : (i / spatial) % channels;
        float z = x[i] + bias[c];
        z = z - s1;
        z = z - s2;
        x[i] = fast_mish(z);
    }
}

template<typename scalar_t, bool ChannelsLast>
__global__ void fused_generic(
    scalar_t* __restrict__ x,
    const scalar_t* __restrict__ bias,
    int64_t n,
    int64_t spatial,
    int channels,
    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) {
        int c = ChannelsLast
            ? i % channels
            : (i / spatial) % channels;

        scalar_t biased = static_cast<scalar_t>(
            static_cast<acc_t>(x[i]) + static_cast<acc_t>(bias[c]));
        scalar_t first = static_cast<scalar_t>(
            static_cast<acc_t>(biased) - s1);
        scalar_t second = static_cast<scalar_t>(
            static_cast<acc_t>(first) - s2);
        acc_t z = static_cast<acc_t>(second);
        acc_t sp = z > acc_t(20) ? z : log1p(exp(z));
        x[i] = static_cast<scalar_t>(z * tanh(sp));
    }
}

torch::Tensor bias_subtract_mish_cuda(
    torch::Tensor x, torch::Tensor bias, double s1, double s2
) {
    TORCH_CHECK(x.is_cuda() && bias.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.dim() == 4, "Expected a four-dimensional tensor");
    TORCH_CHECK(x.device() == bias.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == bias.scalar_type(), "Dtype mismatch");
    TORCH_CHECK(bias.is_contiguous() && bias.numel() == x.size(1),
                "Invalid bias");
    TORCH_CHECK(x.is_contiguous() ||
                x.is_contiguous(at::MemoryFormat::ChannelsLast),
                "Expected contiguous convolution output");

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

    const int channels = x.size(1);
    const int64_t spatial = x.size(2) * x.size(3);
    const bool channels_last =
        !x.is_contiguous() &&
        x.is_contiguous(at::MemoryFormat::ChannelsLast);

    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));
        float* data = x.data_ptr<float>();
        const float* b = bias.data_ptr<float>();

        if (channels_last) {
            fused_float4<true, false><<<blocks, threads, 0, stream>>>(
                data, b, n, spatial, channels, float(s1), float(s2));
        } else if (spatial % 4 == 0) {
            fused_float4<false, true><<<blocks, threads, 0, stream>>>(
                data, b, n, spatial, channels, float(s1), float(s2));
        } else {
            fused_float4<false, false><<<blocks, threads, 0, stream>>>(
                data, b, n, spatial, channels, float(s1), 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(),
            "bias_subtract_mish_cuda",
            [&] {
                if (channels_last) {
                    fused_generic<scalar_t, true>
                        <<<blocks, threads, 0, stream>>>(
                            x.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(),
                            n, spatial, channels, s1, s2);
                } else {
                    fused_generic<scalar_t, false>
                        <<<blocks, threads, 0, stream>>>(
                            x.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(),
                            n, spatial, channels, s1, s2);
                }
            });
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_fused_ops = load_inline(
    name="conv_bias_subtract_mish_v2",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["bias_subtract_mish_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3", "--fmad=false"],
    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 = F.conv2d(
            x,
            self.conv.weight,
            None,
            self.conv.stride,
            self.conv.padding,
            self.conv.dilation,
            self.conv.groups,
        )
        bias = self.conv.bias
        if bias.dtype != x.dtype:
            bias = bias.to(dtype=x.dtype)
        return _fused_ops.bias_subtract_mish_cuda(
            x, bias, self.subtract_value_1, self.subtract_value_2
        )
```