parameters: (self,
                       filename_tensor,
                       vars_to_save,
                       restore_sequentially,
                       reshape,
                       preferred_shard=-1,
                       name="restore_all")
return_statement: return control_flow_ops.group(*assign_ops, name=name)
docstring: Add operations to restore vars_to_save.
  
      Args:
        filename_tensor: Tensor for the path of the file to load.
        vars_to_save: A list of _VarToSave objects.
        restore_sequentially: True if we want to restore variables sequentially
          within a shard.
        reshape: True if we want to reshape loaded tensors to the shape of
          the corresponding variable.
        preferred_shard: Shard to open first when loading a sharded file.
        name: Name for the returned op.
  
      Returns:
        An Operation that restores the variables.
docstring_summary: Add operations to restore vars_to_save.
docstring_tokens: Add operations to restore vars_to_save .
function: def _AddRestoreOps(self,
                       filename_tensor,
                       vars_to_save,
                       restore_sequentially,
                       reshape,
                       preferred_shard=-1,
                       name="restore_all"):
      """Add operations to restore vars_to_save.
  
      Args:
        filename_tensor: Tensor for the path of the file to load.
        vars_to_save: A list of _VarToSave objects.
        restore_sequentially: True if we want to restore variables sequentially
          within a shard.
        reshape: True if we want to reshape loaded tensors to the shape of
          the corresponding variable.
        preferred_shard: Shard to open first when loading a sharded file.
        name: Name for the returned op.
  
      Returns:
        An Operation that restores the variables.
      """
      assign_ops = []
      for vs in vars_to_save:
        v = vs.var
        restore_control_inputs = assign_ops[-1:] if restore_sequentially else []
        # Load and optionally reshape on the CPU, as string tensors are not
        # available on the GPU.
        # TODO(touts): Re-enable restore on GPU when we can support annotating
        # string tensors as "HostMemory" inputs.
        with ops.device(graph_util.set_cpu0(v.device) if v.device else None):
          with ops.control_dependencies(restore_control_inputs):
            values = self.restore_op(filename_tensor, vs, preferred_shard)
          if reshape:
            shape = v.get_shape()
            if not shape.is_fully_defined():
              shape = array_ops.shape(v)
            values = array_ops.reshape(values, shape)
  
        validate_shape = not reshape and v.get_shape().is_fully_defined()
        assign_ops.append(state_ops.assign(v,
                                           values,
                                           validate_shape=validate_shape))
  
      # Create a Noop that has control dependencies from all the updates.
      return control_flow_ops.group(*assign_ops, name=name)
function_tokens: def _AddRestoreOps ( self , filename_tensor , vars_to_save , restore_sequentially , reshape , preferred_shard = - 1 , name = "restore_all" ) : assign_ops = [ ] for vs in vars_to_save : v = vs . var restore_control_inputs = assign_ops [ - 1 : ] if restore_sequentially else [ ] # Load and optionally reshape on the CPU, as string tensors are not # available on the GPU. # TODO(touts): Re-enable restore on GPU when we can support annotating # string tensors as "HostMemory" inputs. with ops . device ( graph_util . set_cpu0 ( v . device ) if v . device else None ) : with ops . control_dependencies ( restore_control_inputs ) : values = self . restore_op ( filename_tensor , vs , preferred_shard ) if reshape : shape = v . get_shape ( ) if not shape . is_fully_defined ( ) : shape = array_ops . shape ( v ) values = array_ops . reshape ( values , shape ) validate_shape = not reshape and v . get_shape ( ) . is_fully_defined ( ) assign_ops . append ( state_ops . assign ( v , values , validate_shape = validate_shape ) ) # Create a Noop that has control dependencies from all the updates. return control_flow_ops . group ( * assign_ops , name = name )