code: def text_embedding_column_v2(
      key, module_path, output_key=None, trainable=False
  ):
      """Uses a TF2 SavedModel to construct a dense representation from text.
  
      Args:
        key: A string or `FeatureColumn` identifying the input string data.
        module_path: A string path to the module. Can be a path to local filesystem
          or a tfhub.dev handle.
        output_key: Name of the output item to return if the layer returns a dict.
          If the result is not a single value and an `output_key` is not specified,
          the feature column cannot infer the right output to use.
        trainable: Whether or not the Model is trainable. False by default, meaning
          the pre-trained weights are frozen. This is different from the ordinary
          tf.feature_column.embedding_column(), but that one is intended for
          training from scratch.
  
      Returns:
        `DenseColumn` that converts from text input.
      """
      if not hasattr(feature_column_v2.StateManager, "has_resource"):
          raise NotImplementedError(
              "The currently used TensorFlow release is not "
              "compatible. To be compatible, the symbol "
              "tensorflow.python.feature_column."
              "feature_column_v2.StateManager.has_resource "
              "must exist."
          )
  
      return _TextEmbeddingColumnV2(
          key=key,
          module_path=module_path,
          output_key=output_key,
          trainable=trainable,
      )
  
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 2 for calling a global function with example usage: text_embedding_column_v2(key, module_path, output_key, trainable) and returns: _TextEmbeddingColumnV2