class_docstr: Abstract base class for models.
  
  Args:
    params: A dictionary of hyperparameter values
    name: A name for this model to be used as a variable scope
class_docstr_tok: ["Abstract","base","class","for","models",".","Args",":","params",":","A","dictionary","of","hyperparameter","values","name",":","A","name","for","this","model","to","be","used","as","a","variable","scope"]
docstr: Returns a dictionary of default parameters for this model.
docstr_tok: ["Returns","a","dictionary","of","default","parameters","for","this","model","."]
code: def default_params():
      """Returns a dictionary of default parameters for this model."""
      return {
          "optimizer.name": "Adam",
          "optimizer.learning_rate": 1e-4,
          "optimizer.params": {},  # Arbitrary parameters for the optimizer
          "optimizer.lr_decay_type": "",
          "optimizer.lr_decay_steps": 100,
          "optimizer.lr_decay_rate": 0.99,
          "optimizer.lr_start_decay_at": 0,
          "optimizer.lr_stop_decay_at": tf.int32.max,
          "optimizer.lr_min_learning_rate": 1e-12,
          "optimizer.lr_staircase": False,
          "optimizer.clip_gradients": 5.0,
          "optimizer.sync_replicas": 0,
          "optimizer.sync_replicas_to_aggregate": 0,
      }
  
file_path: seq2seq/seq2seq/models/model_base.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import collections
  import tensorflow
  from seq2seq.configurable import Configurable
  from seq2seq.training import utils
  from seq2seq import global_vars
label_desc: Use image node_id 5 for calling the ModelBase obj's underlying member method code with example usage: obj.default_params() and returns: dict