Project: s41467-024-45059-8 | Function: forward (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Forward dynamics model with adaptive module that predicts tactile response from haptic signals using personalized latent features

**KEY TERMS:** {
  "act_z": "historical haptic signals for adaptation",
  "tactile_z": "historical tactile responses for adaptation",
  "act": "current haptic signal",
  "z": "user-specific latent feature vector",
  "x": "predicted tactile response"
}

**CONTEXT CODE:** '''
This code defines the forward model (with adaptation module),
which will be used in online_supervise_train.py with oneline_supervise_dataloader.py.
'''

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

class haptac_online(nn.Module):
    def __init__(self, num_input_z, num_input, num_z, num_output):
        super(haptac_online, self).__init__()
        self.linear0_z = nn.Linear(num_input_z * 2,1280) #6x3=18
        self.linear1_z = nn.Linear(1280,2560)
        self.linear2_z = nn.Linear(2560,1280)
        self.linear3_z = nn.Linear(1280,num_z) #6x15=90

        self.linear0 = nn.Linear(num_input + num_z, 1280) #6x3=18
        self.linear1 = nn.Linear(1280,2560)
        self.linear2 = nn.Linear(2560,1280)
        self.linear3 = nn.Linear(1280,num_output) #6x15=90

    def forward(self, act_z, tactile_z, act): #act_z, tactile_z, tactile_goal_z, act
        """Forward pass."""
        # MASKED: Forward dynamics model with adaptive module that predicts tactile response from haptic signals using personalized latent features implementation
        raise NotImplementedError("This function is masked for testing purposes.")


Complete the function implementation replacing "raise NotImplementedError".