PyTorch eager

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

class Model(nn.Module):
    """
    Simple model that performs a HardTanh activation.
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies HardTanh activation to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of any shape.

        Returns:
            torch.Tensor: Output tensor with HardTanh applied, same shape as input.
        """
        return F.hardtanh(x, min_val=-1., max_val=1.)

batch_size = 4096
dim = 393216

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

def get_init_inputs():
    return []  # No special initialization inputs needed