using System;

using System.Collections.Generic;

using System.Text;

namespace Tensorflow.Train

{

public abstract class Trackable

{

protected int _self_update_uid;

/// <summary>

/// Restore-on-create for a variable be saved with this `Checkpointable`.

/// </summary>

/// <returns></returns>

protected virtual RefVariable _add_variable_with_custom_getter(string name,

int[] shape,

TF_DataType dtype = TF_DataType.TF_FLOAT,

IInitializer initializer = null,

Func<string, int[], TF_DataType, IInitializer, bool, RefVariable> getter = null,

bool overwrite = false,

bool trainable = false)

{

var checkpoint_initializer = true;

var new_variable = getter(name, shape, dtype, initializer, trainable);

// If we set an initializer and the variable processed it, tracking will not

// assign again. It will add this variable to our dependencies, and if there

// is a non-trivial restoration queued, it will handle that. This also

// handles slot variables.

if (!overwrite || new_variable is RefVariable)

return _track_checkpointable(new_variable, name: name,

overwrite: overwrite);

else

return new_variable;

}

/// <summary>

/// Pop and load any deferred checkpoint restores into `trackable`.

/// </summary>

/// <param name="name"></param>

/// <param name="trackable"></param>

protected void _handle_deferred_dependencies(string name, RefVariable trackable)

{

_maybe_initialize_trackable();

// TODO

}

protected RefVariable _track_checkpointable(RefVariable checkpointable, string name, bool overwrite = false)

{

return checkpointable;

}

/// <summary>

/// Initialize dependency management.

/// </summary>

protected void _maybe_initialize_trackable()

{

// _self_unconditional_checkpoint_dependencies = []

_self_update_uid = -1;

}

}

}