class_docstr: Implements the Hash Layer FFN.
docstr: Forward pass.
docstr_tok: ["Forward","pass","."]
code: def forward(self, x, orig_input):
      """
      Forward pass.
      """
      xhs = self.hash(orig_input)
  
      # Now do the real work.
      # This implementation could be more efficient -- but it works.
      index_list = [
          torch.eq(xhs, i).nonzero(as_tuple=True)
          for i in range(self.hashsize)
      ]
      final_output = x.new_zeros(x.shape)
  
      for i in range(self.hashsize):
          vecs = x[index_list[i][0], index_list[i][1], :]
          if vecs.shape[0] > 0:
              x1 = self.linears1[i](vecs)
              x1 = self.nonlinear(x1)
              x1 = self.relu_dropout(x1)  # --relu-dropout
              x1 = self.linears2[i](x1)
              final_output[index_list[i][0], index_list[i][1], :] = x1
  
      return final_output
  
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 3 for calling the HashLayerFFN obj's underlying member method code with example usage: obj.forward(x, orig_input) and returns: final_output