```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 fused_activations_cuda(torch::Tensor x, torch::Tensor add);
"""

_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>

template <typename T>
__device__ __forceinline__ T activate(T x);

template <>
__device__ __forceinline__ float activate<float>(float x) {
    x = x / (1.0f + expf(-x));
    x = tanhf(x);
    x = (0.5f * x) *
        (1.0f + erff(x * 0.70710678118654752440f));
    return x < -1.0f ? -1.0f : (x > 1.0f ? 1.0f : x);
}

template <>
__device__ __forceinline__ double activate<double>(double x) {
    x = x / (1.0 + exp(-x));
    x = tanh(x);
    x = (0.5 * x) *
        (1.0 + erf(x * 0.70710678118654752440));
    return x < -1.0 ? -1.0 : (x > 1.0 ? 1.0 : x);
}

__global__ void fused_float4_kernel(
    const float4* __restrict__ x,
    const float4* __restrict__ add,
    float4* __restrict__ out,
    int64_t count,
    int64_t columns
) {
    for (int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
                     + threadIdx.x;
         i < count;
         i += static_cast<int64_t>(blockDim.x) * gridDim.x) {
        float4 v = x[i];
        const float4 a = add[i % columns];
        v.x = activate<float>(v.x + a.x);
        v.y = activate<float>(v.y + a.y);
        v.z = activate<float>(v.z + a.z);
        v.w = activate<float>(v.w + a.w);
        out[i] = v;
    }
}

template <typename scalar_t>
__global__ void fused_scalar_kernel(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ add,
    scalar_t* __restrict__ out,
    int64_t count,
    int64_t columns
) {
    using acc_t = at::acc_type<scalar_t, true>;
    for (int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
                     + threadIdx.x;
         i < count;
         i += static_cast<int64_t>(blockDim.x) * gridDim.x) {
        acc_t v = static_cast<acc_t>(x[i])
                + static_cast<acc_t>(add[i % columns]);
        out[i] = static_cast<scalar_t>(activate<acc_t>(v));
    }
}

torch::Tensor fused_activations_cuda(
    torch::Tensor x,
    torch::Tensor add
) {
    TORCH_CHECK(x.is_cuda() && add.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.device() == add.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == add.scalar_type(), "Dtype mismatch");
    TORCH_CHECK(x.is_contiguous() && add.is_contiguous(),
                "Expected contiguous tensors");
    TORCH_CHECK(x.dim() >= 1 && add.numel() == x.size(-1),
                "Expected one addition value per output feature");

    const c10::cuda::CUDAGuard guard(x.device());
    auto out = torch::empty_like(x);
    const int64_t count = x.numel();
    if (count == 0) {
        return out;
    }

    const int64_t columns = x.size(-1);
    constexpr int threads = 256;
    auto stream = at::cuda::getCurrentCUDAStream();

    if (x.scalar_type() == at::kFloat && columns % 4 == 0 &&
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(add.data_ptr()) % 16 == 0) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + threads - 1) / threads, 4096));
        fused_float4_kernel<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<const float4*>(x.data_ptr<float>()),
            reinterpret_cast<const float4*>(add.data_ptr<float>()),
            reinterpret_cast<float4*>(out.data_ptr<float>()),
            vectors,
            columns / 4);
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads - 1) / threads, 4096));
        AT_DISPATCH_FLOATING_TYPES(
            x.scalar_type(), "fused_activations_cuda", [&] {
                fused_scalar_kernel<scalar_t>
                    <<<blocks, threads, 0, stream>>>(
                        x.data_ptr<scalar_t>(),
                        add.data_ptr<scalar_t>(),
                        out.data_ptr<scalar_t>(),
                        count,
                        columns);
            });
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return out;
}
"""

_fused_ops = load_inline(
    name="linear_swish_tanh_gelu_hardtanh_fused",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fused_activations_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, add_value_shape):
        super().__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.add_value = nn.Parameter(torch.randn(add_value_shape))

    def forward(self, x):
        x = self.matmul(x)

        if (
            x.is_cuda
            and x.dtype in (torch.float32, torch.float64)
            and self.add_value.dtype == x.dtype
            and self.add_value.ndim == 1
            and self.add_value.numel() == x.shape[-1]
        ):
            return _fused_ops.fused_activations_cuda(
                x.contiguous(), self.add_value.contiguous()
            )

        x = x + self.add_value
        x = torch.sigmoid(x) * x
        x = torch.tanh(x)
        x = F.gelu(x)
        return F.hardtanh(x, min_val=-1, max_val=1)
```