class_docstr: Returned by text_embedding_column(). Do not use directly.
class_docstr_tok: ["Returned","by","text_embedding_column","(",")",".","Do","not","use","directly","."]
docstr: Imports the module along with all variables.
docstr_tok: ["Imports","the","module","along","with","all","variables","."]
code: def create_state(self, state_manager):
      """Imports the module along with all variables."""
      # Note: state_manager._trainable is not public but is the pattern used
      # to propagate the "trainable" state that used to be received via
      # self._get_dense_tensor.
      trainable = (
          self.trainable and state_manager._trainable
      )  # pylint: disable=protected-access
      layer = keras_layer.KerasLayer(
          self.module_path,
          output_key=self.output_key,
          trainable=trainable,
      )
      # Note: state manager attaches the loaded resource onto the layer.
      state_manager.add_resource(self, self._resource_name, layer)
      self._variable_shape = _compute_output_shape(
          layer, [None], tf.string
      )
  
file_path: hub/tensorflow_hub/feature_column_v2.py
imports: import collections
  import tensorflow
  from tensorflow_hub import keras_layer
  from tensorflow.python.feature_column import feature_column_v2
label_desc: Use image node_id 5 for calling the _TextEmbeddingColumnV2 obj's underlying member method code with example usage: obj.create_state(state_manager) without return types