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

class Model(nn.Module):
    def __init__(self, beta=2.0):
        super().__init__()
        self.beta = beta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        abs_x = torch.abs(x)
        return torch.where(
            abs_x < self.beta,
            torch.pow(x + self.beta, 2) / (4.0 * self.beta),
            F.relu(x)
        )

batch_size = 128
feature_dim = 1024

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

def get_init_inputs():
    return [2.0]