```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/OpMathType.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 sigmoid_fn(T x);

template <>
__device__ __forceinline__ float sigmoid_fn(float x) {
    return 1.0f / (1.0f + expf(-x));
}

template <>
__device__ __forceinline__ double sigmoid_fn(double x) {
    return 1.0 / (1.0 + exp(-x));
}

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

template <>
__device__ __forceinline__ float tanh_fn(float x) {
    return tanhf(x);
}

template <>
__device__ __forceinline__ double tanh_fn(double x) {
    return tanh(x);
}

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

template <>
__device__ __forceinline__ float erf_fn(float x) {
    return erff(x);
}

template <>
__device__ __forceinline__ double erf_fn(double x) {
    return erf(x);
}

template <typename scalar_t>
__global__ void fused_activations_kernel(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ add,
    scalar_t* __restrict__ output,
    int64_t count,
    int64_t width
) {
    using acc_t = at::opmath_type<scalar_t>;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        // Preserve intermediate rounding for half and bfloat16 tensors.
        acc_t v = acc_t(scalar_t(acc_t(x[i]) + acc_t(add[i % width])));
        acc_t s = acc_t(scalar_t(sigmoid_fn(v)));
        v = acc_t(scalar_t(v * s));
        v = acc_t(scalar_t(tanh_fn(v)));
        v = acc_t(scalar_t(
            v * acc_t(0.5) *
            (acc_t(1) + erf_fn(v * acc_t(0.70710678118654752440)))
        ));
        v = v < acc_t(-1) ? acc_t(-1) : v;
        v = v > acc_t(1) ? acc_t(1) : v;
        output[i] = scalar_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.dim() == 1 &&
                x.size(-1) == add.numel(), "Invalid add_value shape");

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

    constexpr int threads = 256;
    const int blocks = static_cast<int>(
        std::min<int64_t>((count + threads - 1) / threads, 65535)
    );
    const auto stream = at::cuda::getCurrentCUDAStream();

    AT_DISPATCH_FLOATING_TYPES_AND2(
        at::ScalarType::Half, at::ScalarType::BFloat16,
        x.scalar_type(), "fused_activations_cuda", [&] {
            fused_activations_kernel<scalar_t>
                <<<blocks, threads, 0, stream>>>(
                    x.data_ptr<scalar_t>(),
                    add.data_ptr<scalar_t>(),
                    output.data_ptr<scalar_t>(),
                    count,
                    add.numel()
                );
        }
    );
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_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 self.add_value.is_cuda
            and x.dtype == self.add_value.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)
```