import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, alpha: float, beta: float):
        super(Model, self).__init__()
        self.alpha = nn.Parameter(torch.tensor(float(alpha), dtype=torch.float32))
        self.beta = nn.Parameter(torch.tensor(float(beta), dtype=torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        d = torch.zeros_like(x)
        d[..., :, 1:] = x[..., :, 1:] - x[..., :, :-1]
        g = torch.sigmoid(self.alpha * d + self.beta)
        return x * g

N, C, H, W = 8, 64, 64, 64

def get_inputs():
    x = torch.randn(N, C, H, W)
    return [x]

def get_init_inputs():
    return [1.0, 0.0]
