class_docstr: Implements the Hash Layer FFN.
code: def hash(self, xi):
      # Insert your choice of hash function here.
      # In this code we simply randomly hash based on the given token IDs for simplicity.
      if not hasattr(self, "hash_bin_map"):
          # create random mapping.
          sz = self.opt["dict_size"]
          self.hash_bin_map = torch.LongTensor(sz).fill_(0)
          import random
  
          random.seed(42)
          for i in range(sz):
              self.hash_bin_map[i] = random.randrange(0, self.hashsize)
  
      # Now compute the hash bins given the mapping function (Whatever it is).
      return self.hash_bin_map[xi]
  
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 2 for calling the HashLayerFFN obj's underlying member method code with example usage: obj.hash(xi) and returns: self