message: Remove use_resource from v2 Variable doc
  Think v2 Variables are resource Variables so `use_resource=True`
  is not relevant any more (and there is no `use_resource` arg in __init__).
diff: @@ -342,35 +342,6 @@ class Variable(six.with_metaclass(VariableMetaclass,
  `trainable_variables()` returns the contents of this collection. The
  various `Optimizer` classes use this collection as the default list of
  variables to optimize.
  -
  - WARNING: tf.Variable objects by default have a non-intuitive memory model. A
  - Variable is represented internally as a mutable Tensor which can
  - non-deterministically alias other Tensors in a graph. The set of operations
  - which consume a Variable and can lead to aliasing is undetermined and can
  - change across TensorFlow versions. Avoid writing code which relies on the
  - value of a Variable either changing or not changing as other operations
  - happen. For example, using Variable objects or simple functions thereof as
  - predicates in a `tf.cond` is dangerous and error-prone:
  -
  - ```
  - v = tf.Variable(True)
  - tf.cond(v, lambda: v.assign(False), my_false_fn) # Note: this is broken.
  - ```
  -
  - Here, adding `use_resource=True` when constructing the variable will
  - fix any nondeterminism issues:
  -
  - ```
  - v = tf.Variable(True, use_resource=True)
  - tf.cond(v, lambda: v.assign(False), my_false_fn)
  - ```
  -
  - To use the replacement for variables which does
  - not have these issues:
  -
  - * Add `use_resource=True` when constructing `tf.Variable`;
  - * Call `tf.compat.v1.get_variable_scope().set_use_resource(True)` inside a
  - `tf.compat.v1.variable_scope` before the `tf.compat.v1.get_variable()` call.
  """
  def __init__(self,
  