class_docstr: A model that encodes an image and produces a sequence
  of tokens.
class_docstr_tok: ["A","model","that","encodes","an","image","and","produces","a","sequence","of","tokens","."]
code: def default_params():
      params = ModelBase.default_params()
      params.update(
          {
              "attention.class": "AttentionLayerBahdanau",
              "attention.params": {"num_units": 128},
              "bridge.class": "seq2seq.models.bridges.ZeroBridge",
              "bridge.params": {},
              "encoder.class": "seq2seq.encoders.InceptionV3Encoder",
              "encoder.params": {},  # Arbitrary parameters for the encoder
              "decoder.class": "seq2seq.decoders.AttentionDecoder",
              "decoder.params": {},  # Arbitrary parameters for the decoder
              "target.max_seq_len": 50,
              "embedding.dim": 100,
              "inference.beam_search.beam_width": 0,
              "inference.beam_search.length_penalty_weight": 0.0,
              "inference.beam_search.choose_successors_fn": "choose_top_k",
              "vocab_target": "",
          }
      )
      return params
  
file_path: seq2seq/seq2seq/models/image2seq.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import tensorflow
  from seq2seq import graph_utils
  from seq2seq.data import vocab
  from seq2seq.graph_utils import templatemethod
  from seq2seq.models.model_base import ModelBase
  from seq2seq.models.attention_seq2seq import AttentionSeq2Seq
label_desc: Use image node_id 2 for calling the Image2Seq obj's underlying member method code with example usage: obj.default_params() and returns: params