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: Clips gradients by global norm.
docstr_tok: ["Clips","gradients","by","global","norm","."]
code: def _clip_gradients(self, grads_and_vars):
      """Clips gradients by global norm."""
      gradients, variables = zip(*grads_and_vars)
      clipped_gradients, _ = tf.clip_by_global_norm(
          gradients, self.params["optimizer.clip_gradients"]
      )
      return list(zip(clipped_gradients, variables))
  
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 2 for calling the ModelBase obj's underlying member method code with example usage: obj._clip_gradients(grads_and_vars) and returns: list