class_docstr: ADULT encoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of
  two fully connected layers with ReLU and tanh nonlinearities. The tanh nonlinearity clips the embedding in [-1, 1]
  as required in the DDPG algorithm (e.g., [act_low, act_high]). The layers' dimensions used in the paper are
  128 and 15, although those can vary as they were selected to generalize across many datasets.
docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  
  Returns
  -------
  Encoding representation having each component in the interval [-1, 1]
docstr_tok: ["Forward","pass",".","Parameters","--","--","--","--","--","x","Input","tensor",".","Returns","--","--","--","-","Encoding","representation","having","each","component","in","the","interval","[","-1",",","1","]"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Forward pass.
  
      Parameters
      ----------
      x
          Input tensor.
  
      Returns
      -------
      Encoding representation having each component in the interval [-1, 1]
      """
      x = F.relu(self.fc1(x))
      x = torch.tanh(self.fc2(x))
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import List
  from alibi.models.pytorch.model import Model
label_desc: Use image node_id 2 for calling the ADULTEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: x