layer build method

build.py

  def build(self, input_shape):
      self.bert = hub.Module(self.bert_path, trainable=self.trainable, name=f"{self.name}_module")
  
      trainable_layers = []
      if self.tune_embeddings:
          trainable_layers.append("embeddings")
  
      if self.pooling == "cls":
          trainable_layers.append("pooler")
  
      if self.n_tune_layers > 0:
          encoder_var_names = [var.name for var in self.bert.variables if 'encoder' in var.name]
          n_encoder_layers = int(len(encoder_var_names) / self.var_per_encoder)
          for i in range(self.n_tune_layers):
              trainable_layers.append(f"encoder/layer_{str(n_encoder_layers - 1 - i)}/")
      
      # Add module variables to layer's trainable weights
      for var in self.bert.variables:
          if any([l in var.name for l in trainable_layers]):
              self._trainable_weights.append(var)
          else:
              self._non_trainable_weights.append(var)
  
      if self.verbose:
          print("*** TRAINABLE VARS *** ")
          for var in self._trainable_weights:
              print(var)
  
      self.build_preprocessor()
      self.initialize_module()
  
      super(BertLayer, self).build(input_shape)