message: Adjust docs to account for StaticHashTableV1 being a subclass of StaticHashTable
  Docs for both 1.x and 2.x versions of StaticHashTable
diff: @@ -252,22 +252,6 @@ class StaticHashTable(InitializableLookupTableBase):
  tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
  print(table.lookup(input_tensor))
  ```
  -
  - When running in graph mode, you must evaluate the tensor returned by
  - `tf.tables_initializer()` before evaluating the tensor returned by
  - this class's `lookup()` method. Example usage in graph mode:
  -
  - ```python
  - keys_tensor = tf.constant([1, 2])
  - vals_tensor = tf.constant([3, 4])
  - input_tensor = tf.constant([1, 5])
  - table = tf.lookup.StaticHashTable(
  - tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
  - out = table.lookup(input_tensor)
  - with tf.Session() as sess:
  - sess.run(tf.tables_initializer())
  - print(sess.run(out))
  - ```
  """
  def __init__(self, initializer, default_value, name=None):
  @@ -332,6 +316,38 @@ class StaticHashTable(InitializableLookupTableBase):
  @tf_export(v1=["lookup.StaticHashTable"])
  class StaticHashTableV1(StaticHashTable):
  + """A generic hash table implementation. The table is immutable once
  + initialized.
  +
  + When running in graph mode, you must evaluate the tensor returned by
  + `tf.tables_initializer()` before evaluating the tensor returned by
  + this class's `lookup()` method. Example usage in graph mode:
  +
  + ```python
  + keys_tensor = tf.constant([1, 2])
  + vals_tensor = tf.constant([3, 4])
  + input_tensor = tf.constant([1, 5])
  + table = tf.lookup.StaticHashTable(
  + tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
  + out = table.lookup(input_tensor)
  + with tf.Session() as sess:
  + sess.run(tf.tables_initializer())
  + print(sess.run(out))
  + ```
  +
  + In eager mode, no special code is needed to initialize the table.
  + Example usage in eager mode:
  +
  + ```python
  + tf.enable_eager_execution()
  + keys_tensor = tf.constant([1, 2])
  + vals_tensor = tf.constant([3, 4])
  + input_tensor = tf.constant([1, 5])
  + table = tf.lookup.StaticHashTable(
  + tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
  + print(table.lookup(input_tensor))
  + ```
  + """
  @property
  def initializer(self):
  