from tensorflow.contrib.layers.python.layers import feature_column

      }
      labels = constant_op.constant(((1,), (0,), (0,)))
      return features, labels

    # The given hash_bucket_size results in variables larger than the
    # default min_slice_size attribute, so the variables are partitioned.
    sparse_feature = feature_column.sparse_column_with_hash_bucket(
        'language', hash_bucket_size=2e7)
    embedding_feature = feature_column.embedding_column(
        sparse_feature, dimension=1)

    tf_config = {
