import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self, beta=1.0, threshold=20.0):
        super().__init__()
        self.beta = beta
        self.threshold = threshold

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate, act = x.chunk(2, dim=-1)
        return F.softplus(gate, beta=self.beta, threshold=self.threshold) * act

batch_size = 128
feature_dim = 512

def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]

def get_init_inputs():
    return [1.0, 20.0]