parameters: (self,
                 name_or_scope,
                 reuse=None,
                 initializer=None,
                 regularizer=None,
                 caching_device=None,
                 partitioner=None,
                 custom_getter=None,
                 old_name_scope=None,
                 dtype=dtypes.float32,
                 use_resource=None,
                 constraint=None)
docstring: Creates a context for the variable_scope, see `variable_scope` for docs.
  
      Note: this does not create a name scope.
  
      Args:
        name_or_scope: `string` or `VariableScope`: the scope to open.
        reuse: `True` or None, or tf.compat.v1.AUTO_REUSE; if `None`, we inherit
          the parent scope's reuse flag.
        initializer: default initializer for variables within this scope.
        regularizer: default regularizer for variables within this scope.
        caching_device: default caching device for variables within this scope.
        partitioner: default partitioner for variables within this scope.
        custom_getter: default custom getter for variables within this scope.
        old_name_scope: the original name scope when re-entering a variable scope.
        dtype: type of the variables within this scope (defaults to `DT_FLOAT`).
        use_resource: If False, variables in this scope will be regular Variables.
          If True, experimental ResourceVariables will be creates instead, with
          well-defined semantics. Defaults to False (will later change to True).
        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.
docstring_summary: Creates a context for the variable_scope, see `variable_scope` for docs.
docstring_tokens: Creates a context for the variable_scope see variable_scope for docs .
function: def __init__(self,
                 name_or_scope,
                 reuse=None,
                 initializer=None,
                 regularizer=None,
                 caching_device=None,
                 partitioner=None,
                 custom_getter=None,
                 old_name_scope=None,
                 dtype=dtypes.float32,
                 use_resource=None,
                 constraint=None):
      """Creates a context for the variable_scope, see `variable_scope` for docs.
  
      Note: this does not create a name scope.
  
      Args:
        name_or_scope: `string` or `VariableScope`: the scope to open.
        reuse: `True` or None, or tf.compat.v1.AUTO_REUSE; if `None`, we inherit
          the parent scope's reuse flag.
        initializer: default initializer for variables within this scope.
        regularizer: default regularizer for variables within this scope.
        caching_device: default caching device for variables within this scope.
        partitioner: default partitioner for variables within this scope.
        custom_getter: default custom getter for variables within this scope.
        old_name_scope: the original name scope when re-entering a variable scope.
        dtype: type of the variables within this scope (defaults to `DT_FLOAT`).
        use_resource: If False, variables in this scope will be regular Variables.
          If True, experimental ResourceVariables will be creates instead, with
          well-defined semantics. Defaults to False (will later change to True).
        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.
      """
      self._name_or_scope = name_or_scope
      self._reuse = reuse
      self._initializer = initializer
      self._regularizer = regularizer
      self._caching_device = caching_device
      self._partitioner = partitioner
      self._custom_getter = custom_getter
      self._old_name_scope = old_name_scope
      self._dtype = dtype
      self._use_resource = use_resource
      self._constraint = constraint
      self._var_store = _get_default_variable_store()
      self._var_scope_store = get_variable_scope_store()
      self._last_variable_scope_object = None
      if isinstance(self._name_or_scope, VariableScope):
        self._new_name = self._name_or_scope.name
        name_scope = self._name_or_scope._name_scope  # pylint: disable=protected-access
        # Handler for the case when we jump to a shared scope.  We create a new
        #   VariableScope (self._var_scope_object) that contains a copy of the
        #   provided shared scope, possibly with changed reuse and initializer, if
        #   the user requested this.
        variable_scope_object = VariableScope(
            self._name_or_scope.reuse if not self._reuse else self._reuse,
            name=self._new_name,
            initializer=self._name_or_scope.initializer,
            regularizer=self._name_or_scope.regularizer,
            caching_device=self._name_or_scope.caching_device,
            partitioner=self._name_or_scope.partitioner,
            dtype=self._name_or_scope.dtype,
            custom_getter=self._name_or_scope.custom_getter,
            name_scope=name_scope,
            use_resource=self._name_or_scope.use_resource,
            constraint=self._constraint)
        if self._initializer is not None:
          variable_scope_object.set_initializer(self._initializer)
        if self._regularizer is not None:
          variable_scope_object.set_regularizer(self._regularizer)
        if self._caching_device is not None:
          variable_scope_object.set_caching_device(self._caching_device)
        if self._partitioner is not None:
          variable_scope_object.set_partitioner(self._partitioner)
        if self._custom_getter is not None:
          variable_scope_object.set_custom_getter(
              _maybe_wrap_custom_getter(self._custom_getter,
                                        self._name_or_scope.custom_getter))
        if self._dtype is not None:
          variable_scope_object.set_dtype(self._dtype)
        if self._use_resource is not None:
          variable_scope_object.set_use_resource(self._use_resource)
        self._cached_variable_scope_object = variable_scope_object
function_tokens: def __init__ ( self , name_or_scope , reuse = None , initializer = None , regularizer = None , caching_device = None , partitioner = None , custom_getter = None , old_name_scope = None , dtype = dtypes . float32 , use_resource = None , constraint = None ) : self . _name_or_scope = name_or_scope self . _reuse = reuse self . _initializer = initializer self . _regularizer = regularizer self . _caching_device = caching_device self . _partitioner = partitioner self . _custom_getter = custom_getter self . _old_name_scope = old_name_scope self . _dtype = dtype self . _use_resource = use_resource self . _constraint = constraint self . _var_store = _get_default_variable_store ( ) self . _var_scope_store = get_variable_scope_store ( ) self . _last_variable_scope_object = None if isinstance ( self . _name_or_scope , VariableScope ) : self . _new_name = self . _name_or_scope . name name_scope = self . _name_or_scope . _name_scope # pylint: disable=protected-access # Handler for the case when we jump to a shared scope.  We create a new #   VariableScope (self._var_scope_object) that contains a copy of the #   provided shared scope, possibly with changed reuse and initializer, if #   the user requested this. variable_scope_object = VariableScope ( self . _name_or_scope . reuse if not self . _reuse else self . _reuse , name = self . _new_name , initializer = self . _name_or_scope . initializer , regularizer = self . _name_or_scope . regularizer , caching_device = self . _name_or_scope . caching_device , partitioner = self . _name_or_scope . partitioner , dtype = self . _name_or_scope . dtype , custom_getter = self . _name_or_scope . custom_getter , name_scope = name_scope , use_resource = self . _name_or_scope . use_resource , constraint = self . _constraint ) if self . _initializer is not None : variable_scope_object . set_initializer ( self . _initializer ) if self . _regularizer is not None : variable_scope_object . set_regularizer ( self . _regularizer ) if self . _caching_device is not None : variable_scope_object . set_caching_device ( self . _caching_device ) if self . _partitioner is not None : variable_scope_object . set_partitioner ( self . _partitioner ) if self . _custom_getter is not None : variable_scope_object . set_custom_getter ( _maybe_wrap_custom_getter ( self . _custom_getter , self . _name_or_scope . custom_getter ) ) if self . _dtype is not None : variable_scope_object . set_dtype ( self . _dtype ) if self . _use_resource is not None : variable_scope_object . set_use_resource ( self . _use_resource ) self . _cached_variable_scope_object = variable_scope_object