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

_cpp_source = """
#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 <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAStream.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>

template <typename T>
__device__ __forceinline__ T activate(T x) {
    x = x / (T(1) + exp(-x));
    x = tanh(x);
    x = T(0.5) * x * (T(1) + erf(x * T(0.7071067811865475244)));
    return x < T(-1) ? T(-1) : (x > T(1) ? T(1) : 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 = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        float4 v = x[i];
        const float4 a = add[i % columns];
        v.x = activate(v.x + a.x);
        v.y = activate(v.y + a.y);
        v.z = activate(v.z + a.z);
        v.w = activate(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 = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        const acc_t v = acc_t(x[i]) + acc_t(add[i % columns]);
        out[i] = scalar_t(activate(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 additive 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);
    const auto stream = c10::cuda::getCurrentCUDAStream();
    constexpr int threads = 256;

    if (x.scalar_type() == at::kFloat && columns % 4 == 0) {
        const int64_t vectors = count / 4;
        const int blocks = int(std::min<int64_t>(
            (vectors + threads - 1) / threads, 65535));
        fused_float4_kernel<<<blocks, threads, 0, stream.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 = int(std::min<int64_t>(
            (count + threads - 1) / threads, 65535));
        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half, at::ScalarType::BFloat16,
            x.scalar_type(), "fused_activations_cuda", [&] {
                fused_scalar_kernel<scalar_t>
                    <<<blocks, threads, 0, stream.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)
        return _fused_ops.fused_activations_cuda(x, self.add_value)
```