class_docstr: A MultiCell with GNMT attention style.
class_docstr_tok: ["A","MultiCell","with","GNMT","attention","style","."]
docstr: Creates a GNMTAttentionMultiCell.
  
  Args:
    attention_cell: An instance of AttentionWrapper.
    cells: A list of RNNCell wrapped with AttentionInputWrapper.
    use_new_attention: Whether to use the attention generated from current
      step bottom layer's output. Default is False.
docstr_tok: ["Creates","a","GNMTAttentionMultiCell",".","Args",":","attention_cell",":","An","instance","of","AttentionWrapper",".","cells",":","A","list","of","RNNCell","wrapped","with","AttentionInputWrapper",".","use_new_attention",":","Whether","to","use","the","attention","generated","from","current","step","bottom","layer","'s","output",".","Default","is","False","."]
code: def __init__(self, attention_cell, cells, use_new_attention=False):
      """Creates a GNMTAttentionMultiCell.
  
      Args:
        attention_cell: An instance of AttentionWrapper.
        cells: A list of RNNCell wrapped with AttentionInputWrapper.
        use_new_attention: Whether to use the attention generated from current
          step bottom layer's output. Default is False.
      """
      cells = [attention_cell] + cells
      self.use_new_attention = use_new_attention
      super(GNMTAttentionMultiCell, self).__init__(
          cells, state_is_tuple=True
      )
  
file_path: nlp-architect/examples/sparse_gnmt/gnmt_model.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from .gnmt import attention_model
  from .gnmt import model_helper
  from .gnmt.utils import misc_utils
label_desc: Use image node_id 1 to create a new GNMTAttentionMultiCell object from inherited base classes: tf.nn.rnn_cell with example: obj = GNMTAttentionMultiCell(attention_cell, cells, use_new_attention)