import torch
import torch.nn as nn

BATCH_SIZE = 4096
HIDDEN_DIM = 4096
SHAPE = (BATCH_SIZE, HIDDEN_DIM)

ALPHA_VALUE = 3.0

class IpLU(nn.Module):
    '''
    IpLU (Inverse Polynomial Linear Unit) 
    Formula:
      f(x) = x                     if x >= 0
      f(x) = x / (1 + |x|^alpha)   if x < 0
    '''
    def __init__(self, alpha=2.0):
        super(IpLU, self).__init__()
        self.alpha = alpha

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        pos_part = x
        neg_part = x / (1.0 + torch.abs(x).pow(self.alpha))
        return torch.where(x >= 0, pos_part, neg_part)

class Model(nn.Module):
    def __init__(self, alpha=2.0):
        super(Model, self).__init__()
        self.act = IpLU(alpha=alpha)
    
    def forward(self, x):
        return self.act(x)

def get_inputs():
    input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 2.0
    return [input_tensor.contiguous()]

def get_init_inputs():
    return [ALPHA_VALUE]