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.
class_docstr_tok: ["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","."]
docstr: Creates a new variable with value `initial_value`.
  
  The new variable is added to the graph collections listed in `collections`,
  which defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
  
  If `trainable` is `True` the variable is also added to the graph collection
  `GraphKeys.TRAINABLE_VARIABLES`.
  
  This constructor creates both a `variable` Op and an `assign` Op to set the
  variable to its initial value.
  
  Args:
    initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
      which is the initial value for the Variable. The initial value must have
      a shape specified unless `validate_shape` is set to False. Can also be a
      callable with no argument that returns the initial value when called. In
      that case, `dtype` must be specified. (Note that initializer functions
      from init_ops.py must first be bound to a shape before being used here.)
    trainable: If `True`, also adds the variable to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as the default
      list of variables to use by the `Optimizer` classes. Defaults to `True`,
      unless `synchronization` is set to `ON_READ`, in which case it defaults
      to `False`.
    collections: List of graph collections keys. The new variable is added to
      these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
    validate_shape: If `False`, allows the variable to be initialized with a
      value of unknown shape. If `True`, the default, the shape of
      `initial_value` must be known.
    caching_device: Optional device string describing where the Variable
      should be cached for reading.  Defaults to the Variable's device. If not
      `None`, caches on another device.  Typical use is to cache on the device
      where the Ops using the Variable reside, to deduplicate copying through
      `Switch` and other conditional statements.
    name: Optional name for the variable. Defaults to `'Variable'` and gets
      uniquified automatically.
    variable_def: `VariableDef` protocol buffer. If not `None`, recreates the
      Variable object with its contents, referencing the variable's nodes in
      the graph, which must already exist. The graph is not changed.
      `variable_def` and the other arguments are mutually exclusive.
    dtype: If set, initial_value will be converted to the given type. If
      `None`, either the datatype will be kept (if `initial_value` is a
      Tensor), or `convert_to_tensor` will decide.
    expected_shape: A TensorShape. If set, initial_value is expected to have
      this shape.
    import_scope: Optional `string`. Name scope to add to the `Variable.` Only
      used when initializing from protocol buffer.
    constraint: An optional projection function to be applied to the variable
      after being updated by an `Optimizer` (e.g. used to implement norm
      constraints or value constraints for layer weights). The function must
      take as input the unprojected Tensor representing the value of the
      variable and return the Tensor for the projected value (which must have
      the same shape). Constraints are not safe to use when doing asynchronous
      distributed training.
    use_resource: whether to use resource variables.
    synchronization: Indicates when a distributed a variable will be
      aggregated. Accepted values are constants defined in the class
      `tf.VariableSynchronization`. By default the synchronization is set to
      `AUTO` and the current `DistributionStrategy` chooses when to
      synchronize.
    aggregation: Indicates how a distributed variable will be aggregated.
      Accepted values are constants defined in the class
      `tf.VariableAggregation`.
    shape: (optional) The shape of this variable. If None, the shape of
      `initial_value` will be used. When setting this argument to
      `tf.TensorShape(None)` (representing an unspecified shape), the variable
      can be assigned with values of different shapes.
  
  Raises:
    ValueError: If both `variable_def` and initial_value are specified.
    ValueError: If the initial value is not specified, or does not have a
      shape and `validate_shape` is `True`.
    RuntimeError: If eager execution is enabled.
docstr_tok: ["Creates","a","new","variable","with","value","`","initial_value","`",".","The","new","variable","is","added","to","the","graph","collections","listed","in","`","collections","`",",","which","defaults","to","`","[","GraphKeys.GLOBAL_VARIABLES","]","`",".","If","`","trainable","`","is","`","True","`","the","variable","is","also","added","to","the","graph","collection","`","GraphKeys.TRAINABLE_VARIABLES","`",".","This","constructor","creates","both","a","`","variable","`","Op","and","an","`","assign","`","Op","to","set","the","variable","to","its","initial","value",".","Args",":","initial_value",":","A","`","Tensor","`",",","or","Python","object","convertible","to","a","`","Tensor","`",",","which","is","the","initial","value","for","the","Variable",".","The","initial","value","must","have","a","shape","specified","unless","`","validate_shape","`","is","set","to","False",".","Can","also","be","a","callable","with","no","argument","that","returns","the","initial","value","when","called",".","In","that","case",",","`","dtype","`","must","be","specified",".","(","Note","that","initializer","functions","from","init_ops.py","must","first","be","bound","to","a","shape","before","being","used","here",".",")","trainable",":","If","`","True","`",",","also","adds","the","variable","to","the","graph","collection","`","GraphKeys.TRAINABLE_VARIABLES","`",".","This","collection","is","used","as","the","default","list","of","variables","to","use","by","the","`","Optimizer","`","classes",".","Defaults","to","`","True","`",",","unless","`","synchronization","`","is","set","to","`","ON_READ","`",",","in","which","case","it","defaults","to","`","False","`",".","collections",":","List","of","graph","collections","keys",".","The","new","variable","is","added","to","these","collections",".","Defaults","to","`","[","GraphKeys.GLOBAL_VARIABLES","]","`",".","validate_shape",":","If","`","False","`",",","allows","the","variable","to","be","initialized","with","a","value","of","unknown","shape",".","If","`","True","`",",","the","default",",","the","shape","of","`","initial_value","`","must","be","known",".","caching_device",":","Optional","device","string","describing","where","the","Variable","should","be","cached","for","reading",".","Defaults","to","the","Variable","'s","device",".","If","not","`","None","`",",","caches","on","another","device",".","Typical","use","is","to","cache","on","the","device","where","the","Ops","using","the","Variable","reside",",","to","deduplicate","copying","through","`","Switch","`","and","other","conditional","statements",".","name",":","Optional","name","for","the","variable",".","Defaults","to","`","'Variable","'","`","and","gets","uniquified","automatically",".","variable_def",":","`","VariableDef","`","protocol","buffer",".","If","not","`","None","`",",","recreates","the","Variable","object","with","its","contents",",","referencing","the","variable","'s","nodes","in","the","graph",",","which","must","already","exist",".","The","graph","is","not","changed",".","`","variable_def","`","and","the","other","arguments","are","mutually","exclusive",".","dtype",":","If","set",",","initial_value","will","be","converted","to","the","given","type",".","If","`","None","`",",","either","the","datatype","will","be","kept","(","if","`","initial_value","`","is","a","Tensor",")",",","or","`","convert_to_tensor","`","will","decide",".","expected_shape",":","A","TensorShape",".","If","set",",","initial_value","is","expected","to","have","this","shape",".","import_scope",":","Optional","`","string","`",".","Name","scope","to","add","to","the","`","Variable.","`","Only","used","when","initializing","from","protocol","buffer",".","constraint",":","An","optional","projection","function","to","be","applied","to","the","variable","after","being","updated","by","an","`","Optimizer","`","(","e.g",".","used","to","implement","norm","constraints","or","value","constraints","for","layer","weights",")",".","The","function","must","take","as","input","the","unprojected","Tensor","representing","the","value","of","the","variable","and","return","the","Tensor","for","the","projected","value","(","which","must","have","the","same","shape",")",".","Constraints","are","not","safe","to","use","when","doing","asynchronous","distributed","training",".","use_resource",":","whether","to","use","resource","variables",".","synchronization",":","Indicates","when","a","distributed","a","variable","will","be","aggregated",".","Accepted","values","are","constants","defined","in","the","class","`","tf.VariableSynchronization","`",".","By","default","the","synchronization","is","set","to","`","AUTO","`","and","the","current","`","DistributionStrategy","`","chooses","when","to","synchronize",".","aggregation",":","Indicates","how","a","distributed","variable","will","be","aggregated",".","Accepted","values","are","constants","defined","in","the","class","`","tf.VariableAggregation","`",".","shape",":","(","optional",")","The","shape","of","this","variable",".","If","None",",","the","shape","of","`","initial_value","`","will","be","used",".","When","setting","this","argument","to","`","tf.TensorShape","(","None",")","`","(","representing","an","unspecified","shape",")",",","the","variable","can","be","assigned","with","values","of","different","shapes",".","Raises",":","ValueError",":","If","both","`","variable_def","`","and","initial_value","are","specified",".","ValueError",":","If","the","initial","value","is","not","specified",",","or","does","not","have","a","shape","and","`","validate_shape","`","is","`","True","`",".","RuntimeError",":","If","eager","execution","is","enabled","."]
code: def __init__(
      self,  # pylint: disable=super-init-not-called
      initial_value=None,
      trainable=None,
      collections=None,
      validate_shape=True,
      caching_device=None,
      name=None,
      variable_def=None,
      dtype=None,
      expected_shape=None,
      import_scope=None,
      constraint=None,
      use_resource=None,
      synchronization=variables.VariableSynchronization.AUTO,
      aggregation=variables.VariableAggregation.NONE,
      shape=None,
  ):
      """Creates a new variable with value `initial_value`.
  
      The new variable is added to the graph collections listed in `collections`,
      which defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
  
      If `trainable` is `True` the variable is also added to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES`.
  
      This constructor creates both a `variable` Op and an `assign` Op to set the
      variable to its initial value.
  
      Args:
        initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
          which is the initial value for the Variable. The initial value must have
          a shape specified unless `validate_shape` is set to False. Can also be a
          callable with no argument that returns the initial value when called. In
          that case, `dtype` must be specified. (Note that initializer functions
          from init_ops.py must first be bound to a shape before being used here.)
        trainable: If `True`, also adds the variable to the graph collection
          `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as the default
          list of variables to use by the `Optimizer` classes. Defaults to `True`,
          unless `synchronization` is set to `ON_READ`, in which case it defaults
          to `False`.
        collections: List of graph collections keys. The new variable is added to
          these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
        validate_shape: If `False`, allows the variable to be initialized with a
          value of unknown shape. If `True`, the default, the shape of
          `initial_value` must be known.
        caching_device: Optional device string describing where the Variable
          should be cached for reading.  Defaults to the Variable's device. If not
          `None`, caches on another device.  Typical use is to cache on the device
          where the Ops using the Variable reside, to deduplicate copying through
          `Switch` and other conditional statements.
        name: Optional name for the variable. Defaults to `'Variable'` and gets
          uniquified automatically.
        variable_def: `VariableDef` protocol buffer. If not `None`, recreates the
          Variable object with its contents, referencing the variable's nodes in
          the graph, which must already exist. The graph is not changed.
          `variable_def` and the other arguments are mutually exclusive.
        dtype: If set, initial_value will be converted to the given type. If
          `None`, either the datatype will be kept (if `initial_value` is a
          Tensor), or `convert_to_tensor` will decide.
        expected_shape: A TensorShape. If set, initial_value is expected to have
          this shape.
        import_scope: Optional `string`. Name scope to add to the `Variable.` Only
          used when initializing from protocol buffer.
        constraint: An optional projection function to be applied to the variable
          after being updated by an `Optimizer` (e.g. used to implement norm
          constraints or value constraints for layer weights). The function must
          take as input the unprojected Tensor representing the value of the
          variable and return the Tensor for the projected value (which must have
          the same shape). Constraints are not safe to use when doing asynchronous
          distributed training.
        use_resource: whether to use resource variables.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses when to
          synchronize.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        shape: (optional) The shape of this variable. If None, the shape of
          `initial_value` will be used. When setting this argument to
          `tf.TensorShape(None)` (representing an unspecified shape), the variable
          can be assigned with values of different shapes.
  
      Raises:
        ValueError: If both `variable_def` and initial_value are specified.
        ValueError: If the initial value is not specified, or does not have a
          shape and `validate_shape` is `True`.
        RuntimeError: If eager execution is enabled.
      """
  
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 1 to create a new VariableV1 object from inherited base classes: variables with example: obj = VariableV1(initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint, use_resource, synchronization, aggregation, shape)