class_docstr: See the [Variables Guide](https://tensorflow.org/guide/variables).
  
  A variable maintains state in the graph across calls to `run()`. You add a
  variable to the graph by constructing an instance of the class `Variable`.
  
  The `Variable()` constructor requires an initial value for the variable,
  which can be a `Tensor` of any type and shape. The initial value defines the
  type and shape of the variable. After construction, the type and shape of
  the variable are fixed. The value can be changed using one of the assign
  methods.
  
  If you want to change the shape of a variable later you have to use an
  `assign` Op with `validate_shape=False`.
  
  Just like any `Tensor`, variables created with `Variable()` can be used as
  inputs for other Ops in the graph. Additionally, all the operators
  overloaded for the `Tensor` class are carried over to variables, so you can
  also add nodes to the graph by just doing arithmetic on variables.
  
  ```python
  import tensorflow as tf
  
  # Create a variable.
  w = tf.Variable(<initial-value>, name=<optional-name>)
  
  # Use the variable in the graph like any Tensor.
  y = tf.matmul(w, ...another variable or tensor...)
  
  # The overloaded operators are available too.
  z = tf.sigmoid(w + y)
  
  # Assign a new value to the variable with `assign()` or a related method.
  w.assign(w + 1.0)
  w.assign_add(1.0)
  ```
  
  When you launch the graph, variables have to be explicitly initialized before
  you can run Ops that use their value. You can initialize a variable by
  running its *initializer op*, restoring the variable from a save file, or
  simply running an `assign` Op that assigns a value to the variable. In fact,
  the variable *initializer op* is just an `assign` Op that assigns the
  variable's initial value to the variable itself.
  
  ```python
  # Launch the graph in a session.
  with tf.compat.v1.Session() as sess:
      # Run the variable initializer.
      sess.run(w.initializer)
      # ...you now can run ops that use the value of 'w'...
  ```
  
  The most common initialization pattern is to use the convenience function
  `global_variables_initializer()` to add an Op to the graph that initializes
  all the variables. You then run that Op after launching the graph.
  
  ```python
  # Add an Op to initialize global variables.
  init_op = tf.compat.v1.global_variables_initializer()
  
  # Launch the graph in a session.
  with tf.compat.v1.Session() as sess:
      # Run the Op that initializes global variables.
      sess.run(init_op)
      # ...you can now run any Op that uses variable values...
  ```
  
  If you need to create a variable with an initial value dependent on another
  variable, use the other variable's `initialized_value()`. This ensures that
  variables are initialized in the right order.
  
  All variables are automatically collected in the graph where they are
  created. By default, the constructor adds the new variable to the graph
  collection `GraphKeys.GLOBAL_VARIABLES`. The convenience function
  `global_variables()` returns the contents of that collection.
  
  When building a machine learning model it is often convenient to distinguish
  between variables holding the trainable model parameters and other variables
  such as a `global step` variable used to count training steps. To make this
  easier, the variable constructor supports a `trainable=<bool>` parameter. If
  `True`, the new variable is also added to the graph collection
  `GraphKeys.TRAINABLE_VARIABLES`. The convenience function
  `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.
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code: def initialized_value(self):
      # variable_v1.py is imported at the top-level internally at TF1 import time,
      # so the import time for this file should be reduced as much as possible.
      # Thus import cond only when it is used.
      from tensorflow.python.ops import (
          cond,
      )  # pylint: disable=g-import-not-at-top
  
      with ops.init_scope():
          return cond.cond(
              is_variable_initialized(self),
              self.read_value,
              lambda: self.initial_value,
          )
  
file_path: tensorflow-upstream/tensorflow/python/ops/variable_v1.py
imports: from tensorflow.python.framework import ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.util import tf_should_use
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling the VariableV1 obj's underlying member method code with example usage: obj.initialized_value() and returns: cond