class_docstr: Actual model code for the Transresnet Agent.
code: def __init__(self, opt, personalities_list, dictionary):
      super().__init__()
      self.use_cuda = not opt["no_cuda"] and torch.cuda.is_available()
      self.opt = opt
      self.dictionary = dictionary
      self.truncate_length = opt["truncate"]
      if opt["num_layers_all"] != -1:
          n_layers_text = n_layers_img = opt["num_layers_all"]
      else:
          n_layers_text = opt["num_layers_text_encoder"]
          n_layers_img = opt["num_layers_image_encoder"]
      self.text_encoder_frozen = False
  
      # Initialize personalities dictionary
      self._build_personality_dictionary(personalities_list)
  
      # Text encoder
      self._build_text_encoder(n_layers_text)
  
      # Image encoder
      self._build_image_encoder(n_layers_img)
  
      # Personality Encoder
      self._build_personality_encoder()
  
      # optimizer
      self.optimizer = optim.Adam(
          filter(lambda p: p.requires_grad, self.parameters()),
          self.opt["learningrate"],
      )
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from torch import optim
  from parlai.agents.transformer.modules import TransformerEncoder
  from parlai.agents.transformer import transformer
  from parlai.utils.io import PathManager
label_desc: Use image node_id 2 to create a new TransresnetModel object from inherited base classes: nn with example: obj = TransresnetModel(opt, personalities_list, dictionary)