You write custom CUDA kernels to replace the PyTorch operators in the given SELU-Clip activation architecture to get speedups.
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace the combined SELU calculation and clamp operators with a custom CUDA kernel (considering operator fusion opportunities to combine the element-wise SELU computation and range clipping into a single kernel) or adjust algorithms for better performance. You are only limited by your imagination.

Here's an example to show you the syntax of inline embedding custom CUDA operators in PyTorch (for reference of code structure, not functional alignment):
The example given architecture (sample structure):
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
    def forward(self, a, b):
        return a + b
def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]
def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []

The example new arch with custom CUDA kernels (sample structure):
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
# Define custom CUDA kernel and load it inline
custom_add_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>
__global__ void custom_add_kernel(const float* a, const float* b, float* out, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        out[idx] = a[idx] + b[idx];
    }
}
torch::Tensor custom_add_cuda(torch::Tensor a, torch::Tensor b) {
    auto size = a.numel();
    auto out = torch::empty_like(a);
    const int block_size = 256;
    int num_blocks = (size + block_size - 1) / block_size;
    custom_add_kernel<<<num_blocks, block_size>>>(a.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), size);
    return out;
}
"""
custom_add_cpp_source = "torch::Tensor custom_add_cuda(torch::Tensor a, torch::Tensor b);"
custom_add = load_inline(
    name="custom_add",
    cpp_sources=custom_add_cpp_source,
    cuda_sources=custom_add_source,
    functions=["custom_add_cuda"],
    verbose=True
)
class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.custom_add = custom_add
    def forward(self, a, b):
        return self.custom_add.custom_add_cuda(a, b)
def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]
def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []

You are given the following SELU-Clip activation architecture (base PyTorch implementation):
import torch
import torch.nn as nn
class Model(nn.Module):
    """
    SELU-Clip activation function: Mathematical formulation is clamp(scale * (x if x > 0 else alpha*(exp(x)-1)), clip_min, clip_max),
    where alpha=1.6732632423543772 (SELU shape parameter), scale=1.0507009873554804 (SELU scaling parameter),
    clip_min=-5.0 (minimum clipping value), clip_max=5.0 (maximum clipping value).
    """
    def __init__(self, alpha=1.6732632423543772, scale=1.0507009873554804, clip_min=-5.0, clip_max=5.0):
        super(Model, self).__init__()
        self.alpha = alpha
        self.scale = scale
        self.clip_min = clip_min
        self.clip_max = clip_max

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies SELU-Clip activation to the input tensor.
        Args:
            x (torch.Tensor): Input tensor with fixed shape (batch_size, dim)
                              where batch_size=1024 and dim=2048.
        Returns:
            torch.Tensor: Output tensor with SELU-Clip applied, same shape as input.
        """
        # Calculate SELU
        selu_out = self.scale * torch.where(
            x > 0,
            x,
            self.alpha * (torch.exp(x) - 1)
        )
        # Apply clipping
        clipped_out = torch.clamp(selu_out, self.clip_min, self.clip_max)
        return clipped_out

batch_size = 1024
dim = 2048
def get_inputs():
    # Randomly generate input tensor matching the fixed shape (batch_size, dim)
    x = torch.randn(batch_size, dim)
    return [x]
def get_init_inputs():
    # Provide initialization parameters for the model (non-trainable hyperparameters)
    return (1.6732632423543772, 1.0507009873554804, -5.0, 5.0)