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

class Model(nn.Module):
    def __init__(self, scale, shift, negative_slope, min_val, max_val):
        super(Model, self).__init__()
        self.scale = scale
        self.shift = shift
        self.negative_slope = negative_slope
        self.min_val = min_val
        self.max_val = max_val

    def forward(self, x):
        x = x * self.scale + self.shift
        x = F.leaky_relu(x, negative_slope=self.negative_slope)
        return torch.clamp(x, self.min_val, self.max_val)

batch_size = 1024
dim = 1024

def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]

def get_init_inputs():
    return [2.0, 0.5, 0.1, -1.0, 1.0]