class FailedPreconditionError(OpError):
  """Operation was rejected because the system is not in a state to execute it.
  This exception is most commonly raised when running an operation
  that reads a @{tf.Variable}
  before it has been initialized.
  @@__init__
  """

  def __init__(self, node_def, op, message):
    """Creates a `FailedPreconditionError`."""
    super(FailedPreconditionError, self).__init__(node_def, op, message, FAILED_PRECONDITION)

def global_variables_initializer():
  """Returns an Op that initializes global variables.
  This is just a shortcut for `variables_initializer(global_variables())`
  Returns:
    An Op that initializes global variables in the graph.
  """
  if context.executing_eagerly():
    return control_flow_ops.no_op(name="global_variables_initializer")
  return variables_initializer(global_variables())

def variables_initializer(var_list, name="init"):
  """Returns an Op that initializes a list of variables.
  After you launch the graph in a session, you can run the returned Op to
  initialize all the variables in `var_list`. This Op runs all the
  initializers of the variables in `var_list` in parallel.
  Calling `initialize_variables()` is equivalent to passing the list of
  initializers to `Group()`.
  If `var_list` is empty, however, the function still returns an Op that can
  be run. That Op just has no effect.
  Args:
    var_list: List of `Variable` objects to initialize.
    name: Optional name for the returned operation.
  Returns:
    An Op that run the initializers of all the specified variables.
  """
  if var_list and not context.executing_eagerly():
    return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
  return control_flow_ops.no_op(name=name)

def global_variables(scope=None):
  """Returns global variables.
  Global variables are variables that are shared across machines in a
  distributed environment. The `Variable()` constructor or `get_variable()`
  automatically adds new variables to the graph collection
  `GraphKeys.GLOBAL_VARIABLES`.
  This convenience function returns the contents of that collection.
  An alternative to global variables are local variables. See
  @{tf.local_variables}
  Args:
    scope: (Optional.) A string. If supplied, the resulting list is filtered
      to include only items whose `name` attribute matches `scope` using
      `re.match`. Items without a `name` attribute are never returned if a
      scope is supplied. The choice of `re.match` means that a `scope` without
      special tokens filters by prefix.
  Returns:
    A list of `Variable` objects.
  """
  return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope)

session.run(tf.global_variables_initializer())
# Now all variables are initialized.

v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())
w = tf.get_variable("w", initializer=v.initialized_value() + 1)