class_docstr: Transformer Encoder is stacked with
  several TransformerencLayers.
class_docstr_tok: ["Transformer","Encoder","is","stacked","with","several","TransformerencLayers","."]
docstr: Input shape: [Batch size, enc_seq_len]
  Output shape: [Batch size, enc_seq_len, hidden_dim]
docstr_tok: ["Input","shape",":","[","Batch","size",",","enc_seq_len","]","Output","shape",":","[","Batch","size",",","enc_seq_len",",","hidden_dim","]"]
code: def call(self, inps, training=None, mask=None):
      """
      Input shape: [Batch size, enc_seq_len]
      Output shape: [Batch size, enc_seq_len, hidden_dim]
      """
      enc_inps = inps
      for enc_layer in self.transformer_encs:
          enc_inps = enc_layer(enc_inps, training=training, mask=mask)
      enc_outs = enc_inps
      return enc_outs
  
imports: import math
  from absl import logging
  import numpy
  import delta.compat
  from tensorflow.python.util import nest
  from delta.layers.base_layer import Layer
  from delta.layers import utils_tf
  import delta.layers
label_desc: Use image node_id 2 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.call(inps, training, mask) and returns: enc_outs