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

_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>

__device__ __forceinline__ float activate(
    float x, float multiplier, float slope
) {
    float y = x * multiplier;
    return y > 0.0f ? y : y * slope;
}

__global__ void fused_float4_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t n,
    float multiplier,
    float slope
) {
    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;
    const int64_t vectors = n / 4;

    for (int64_t i = index; i < vectors; i += stride) {
        float4 value = reinterpret_cast<const float4*>(input)[i];
        value.x = activate(value.x, multiplier, slope);
        value.y = activate(value.y, multiplier, slope);
        value.z = activate(value.z, multiplier, slope);
        value.w = activate(value.w, multiplier, slope);
        reinterpret_cast<float4*>(output)[i] = value;
    }

    for (int64_t i = vectors * 4 + index; i < n; i += stride) {
        output[i] = activate(input[i], multiplier, slope);
    }
}

template <typename scalar_t>
__global__ void fused_scalar_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    int64_t n,
    double multiplier,
    double slope
) {
    using acc_t = at::acc_type<scalar_t, true>;
    const int64_t stride =
        static_cast<int64_t>(blockDim.x) * gridDim.x;

    for (int64_t i =
             static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
         i < n;
         i += stride) {
        // Preserve the intermediate rounding of the original multiplication.
        scalar_t scaled = static_cast<scalar_t>(
            static_cast<acc_t>(input[i]) * static_cast<acc_t>(multiplier)
        );
        acc_t value = static_cast<acc_t>(scaled);
        output[i] = static_cast<scalar_t>(
            value > acc_t(0) ? value : value * static_cast<acc_t>(slope)
        );
    }
}

torch::Tensor fused_activation(
    torch::Tensor input, double multiplier, double slope
) {
    TORCH_CHECK(input.is_cuda(), "input must be a CUDA tensor");
    TORCH_CHECK(input.is_contiguous(), "input must be contiguous");

    const c10::cuda::CUDAGuard guard(input.device());
    auto output = torch::empty_like(input);
    const int64_t n = input.numel();
    if (n == 0) {
        return output;
    }

    constexpr int threads = 256;
    const bool vectorized = input.scalar_type() == at::kFloat;
    const int64_t work = vectorized ? (n + 3) / 4 : n;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work + threads - 1) / threads, 4096)
    );
    cudaStream_t stream =
        c10::cuda::getCurrentCUDAStream(input.get_device());

    if (vectorized) {
        fused_float4_kernel<<<blocks, threads, 0, stream>>>(
            input.data_ptr<float>(),
            output.data_ptr<float>(),
            n,
            static_cast<float>(multiplier),
            static_cast<float>(slope)
        );
    } else {
        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half,
            at::ScalarType::BFloat16,
            input.scalar_type(),
            "fused_multiply_leaky_relu",
            [&] {
                fused_scalar_kernel<scalar_t>
                    <<<blocks, threads, 0, stream>>>(
                        input.data_ptr<scalar_t>(),
                        output.data_ptr<scalar_t>(),
                        n,
                        multiplier,
                        slope
                    );
            }
        );
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_fused_ops = load_inline(
    name="gemm_mul_leaky_relu_fused_v1",
    cpp_sources=(
        "torch::Tensor fused_activation("
        "torch::Tensor input, double multiplier, double slope);"
    ),
    cuda_sources=_cuda_source,
    functions=["fused_activation"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _FusedActivation(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, multiplier, negative_slope):
        ctx.multiplier = multiplier
        ctx.negative_slope = negative_slope
        if ctx.needs_input_grad[0]:
            ctx.save_for_backward(x)
        return _fused_ops.fused_activation(x, multiplier, negative_slope)

    @staticmethod
    def backward(ctx, grad_output):
        (x,) = ctx.saved_tensors
        scaled = x * ctx.multiplier
        grad = torch.where(
            scaled > 0,
            grad_output,
            grad_output * ctx.negative_slope,
        )
        return grad * ctx.multiplier, None, None


class ModelNew(nn.Module):
    def __init__(
        self, in_features, out_features, multiplier, negative_slope
    ):
        super().__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = multiplier
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, x):
        x = self.gemm(x)
        if not x.is_cuda:
            return self.leaky_relu(x * self.multiplier)
        return _FusedActivation.apply(
            x.contiguous(), self.multiplier, self.leaky_relu.negative_slope
        )
```