indicator_column encodes the input to a multi-hot representation, not one-hot encoding.

The example from https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column clarifies more:

name = indicator_column(categorical_column_with_vocabulary_list(
    'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

dense_tensor == [[1, 0, 0]]  # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]]  # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]]  # If "name" bytes_list is ["bob", "bob"] 

The last two examples describe what is meant by multi-hot encoding. for example if the input be ["bob", "wanda"] the encoding will be [[1, 0, 1]].

indicator_column encodes the input to a multi-hot representation, not one-hot encoding.

The example clarifies more:

name = indicator_column(categorical_column_with_vocabulary_list(
    'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

dense_tensor == [[1, 0, 0]]  # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]]  # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]]  # If "name" bytes_list is ["bob", "bob"] 

The last two examples describe what is meant by multi-hot encoding. for example if the input be ["bob", "wanda"] the encoding will be [[1, 0, 1]].