parameters: (self,
                 initial_value=None,
                 trainable=True,
                 collections=None,
                 validate_shape=True,
                 caching_device=None,
                 name=None,
                 variable_def=None,
                 dtype=None,
                 expected_shape=None)
docstring: Creates a new variable with value `initial_value`.
  
      The new variable is added to the graph collections listed in `collections`,
      which defaults to `[GraphKeys.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`, the default, 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.
        collections: List of graph collections keys. The new variable is added to
          these collections. Defaults to `[GraphKeys.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. `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.
  
      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`.
docstring_summary: Creates a new variable with value `initial_value`.
docstring_tokens: Creates a new variable with value initial_value .
function: def __init__(self,
                 initial_value=None,
                 trainable=True,
                 collections=None,
                 validate_shape=True,
                 caching_device=None,
                 name=None,
                 variable_def=None,
                 dtype=None,
                 expected_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.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`, the default, 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.
        collections: List of graph collections keys. The new variable is added to
          these collections. Defaults to `[GraphKeys.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. `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.
  
      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`.
      """
      if variable_def:
        # If variable_def is provided, recreates the variable from its fields.
        if initial_value:
          raise ValueError("variable_def and initial_value are mutually "
                           "exclusive.")
        self._init_from_proto(variable_def)
      else:
        # Create from initial_value.
        self._init_from_args(
            initial_value=initial_value,
            trainable=trainable,
            collections=collections,
            validate_shape=validate_shape,
            caching_device=caching_device,
            name=name,
            dtype=dtype,
            expected_shape=expected_shape)
function_tokens: def __init__ ( self , initial_value = None , trainable = True , collections = None , validate_shape = True , caching_device = None , name = None , variable_def = None , dtype = None , expected_shape = None ) : if variable_def : # If variable_def is provided, recreates the variable from its fields. if initial_value : raise ValueError ( "variable_def and initial_value are mutually " "exclusive." ) self . _init_from_proto ( variable_def ) else : # Create from initial_value. self . _init_from_args ( initial_value = initial_value , trainable = trainable , collections = collections , validate_shape = validate_shape , caching_device = caching_device , name = name , dtype = dtype , expected_shape = expected_shape )