using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using System.Xml.Linq;

using Tensorflow.Contexts;

using static Tensorflow.ApiDef.Types;

using static Tensorflow.CostGraphDef.Types;

using static Tensorflow.Binding;

using Tensorflow.Gradients;

namespace Tensorflow.Eager

{

internal static class _execute

{

public static (DataType[], Tensor[]) onvert_to_mixed_eager_tensors(Tensor[] values, Context ctx)

{

var v = values.Select(t => ops.convert_to_tensor(t, ctx:ctx));

var types = v.Select(t => t.dtype.as_datatype_enum());

return (types.ToArray(), v.ToArray());

}

public static Tensor[] execute(string op_name, int num_outputs, Tensor[] inputs, object[] attrs, Context ctx, string name = null)

{

return quick_execute(op_name, num_outputs, inputs, attrs, ctx, name);

}

public static Tensor[] quick_execute(string op_name, int num_outputs, Tensor[] inputs, object[] attrs, Context ctx, string name = null)

{

string device_name = ctx.DeviceName;

ctx.ensure_initialized();

var tensors = tf.Runner.TFE_Execute(ctx, device_name, op_name, inputs, attrs, num_outputs);

return tensors;

}

public static bool must_record_gradient()

{

return tf.GetTapeSet().Count != 0;

}

public static bool record_gradient(string op_name, Tensor[] inputs, object[] attrs, Tensor[] results)

{

return tf.Runner.RecordGradient(op_name, inputs, attrs, results);

}

}

}