parameters: (self)
docstring: Returns the value of the initialized variable.
  
      You should use this instead of the variable itself to initialize another
      variable with a value that depends on the value of this variable.
  
      ```python
      # Initialize 'v' with a random tensor.
      v = tf.Variable(tf.truncated_normal([10, 40]))
      # Use `initialized_value` to guarantee that `v` has been
      # initialized before its value is used to initialize `w`.
      # The random values are picked only once.
      w = tf.Variable(v.initialized_value() * 2.0)
      ```
  
      Returns:
        A `Tensor` holding the value of this variable after its initializer
        has run.
docstring_summary: Returns the value of the initialized variable.
docstring_tokens: Returns the value of the initialized variable .
function: def initialized_value(self):
      """Returns the value of the initialized variable.
  
      You should use this instead of the variable itself to initialize another
      variable with a value that depends on the value of this variable.
  
      ```python
      # Initialize 'v' with a random tensor.
      v = tf.Variable(tf.truncated_normal([10, 40]))
      # Use `initialized_value` to guarantee that `v` has been
      # initialized before its value is used to initialize `w`.
      # The random values are picked only once.
      w = tf.Variable(v.initialized_value() * 2.0)
      ```
  
      Returns:
        A `Tensor` holding the value of this variable after its initializer
        has run.
      """
      with ops.control_dependencies(None):
        return control_flow_ops.cond(is_variable_initialized(self),
                                     self.read_value,
                                     lambda: self.initial_value)
function_tokens: def initialized_value ( self ) : with ops . control_dependencies ( None ) : return control_flow_ops . cond ( is_variable_initialized ( self ) , self . read_value , lambda : self . initial_value )