code: def hub_models(cls):
      return {
          "conv.stories.pretrained": {
              "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
              "checkpoint_file": "pretrained_checkpoint.pt",
              "tokenizer": "nltk",
          },
          "conv.stories": {
              "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
              "checkpoint_file": "fusion_checkpoint.pt",
              "tokenizer": "nltk",
              "pretrained": "True",
              "pretrained_checkpoint": "./pretrained_checkpoint.pt",
          },
          # Test set containing dictionaries
          "data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2",
      }
  
imports: import logging
  import math
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import checkpoint_utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.models import CompositeEncoder, FairseqDecoder, FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import DownsampledMultiHeadAttention, FairseqDropout, GradMultiply, LayerNorm, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 1 for calling the FConvModelSelfAtt obj's underlying member method code with example usage: obj.hub_models(cls) and returns: dict