class_docstr: Implements the Hash Layer FFN.
code: def __init__(
      self, opt, dim, dim_hidden, relu_dropout=0, activation="relu"
  ):
      super(HashLayerFFN, self).__init__()
      self.relu_dropout = nn.Dropout(p=relu_dropout)
      self.nonlinear = F.relu
      self.opt = opt
      self.dim = dim
      self.dim_hidden = dim_hidden
      self.hashsize = opt["hash_size"]
  
      linears1 = []
      linears2 = []
  
      for i in range(0, self.hashsize):
          linears1.append(nn.Linear(dim, dim_hidden))
          nn.init.xavier_uniform_(linears1[i].weight)
      for i in range(0, self.hashsize):
          linears2.append(nn.Linear(dim_hidden, dim))
          nn.init.xavier_uniform_(linears2[i].weight)
  
      self.linears1 = nn.ModuleList(linears1)
      self.linears2 = nn.ModuleList(linears2)
  
imports: from __future__ import annotations
  import torch
  from typing import Dict, Optional, Tuple
  import torch.nn
  from parlai.agents.transformer.modules import TransformerDecoder, TransformerGeneratorModel
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new HashLayerFFN object from inherited base classes: nn with example: obj = HashLayerFFN(opt, dim, dim_hidden, relu_dropout, activation)