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: Constructor.
  
  Parameters
  ----------
  hidden_dim
      Hidden dimension.
  latent_dim
      Latent dimension.
docstr_tok: ["Constructor",".","Parameters","--","--","--","--","--","hidden_dim","Hidden","dimension",".","latent_dim","Latent","dimension","."]
code: def __init__(self, hidden_dim: int, latent_dim: int):
      """
      Constructor.
  
      Parameters
      ----------
      hidden_dim
          Hidden dimension.
      latent_dim
          Latent dimension.
      """
      super().__init__()
      self.fc1 = nn.LazyLinear(hidden_dim)
      self.fc2 = nn.LazyLinear(latent_dim)
  
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 1 to create a new ADULTEncoder object from inherited base classes: nn with example: obj = ADULTEncoder(hidden_dim, latent_dim)