Mteric and losses skeletonized

src/TensorFlowNET.Keras/Losses/Loss.cs

@@ -6,5 +6,36 @@ namespace Tensorflow.Keras.Losses
 {
     public abstract class Loss
     {
+        public static Tensor mean_squared_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_absolute_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_absolute_percentage_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_squared_logarithmic_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor _maybe_convert_labels(Tensor y_true) => throw new NotImplementedException();
+
+        public static Tensor squared_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor categorical_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor huber_loss(Tensor y_true, Tensor y_pred, float delta = 1) => throw new NotImplementedException();
+
+        public static Tensor logcosh(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
+
+        public static Tensor sparse_categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float axis = -1) => throw new NotImplementedException();
+
+        public static Tensor binary_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
+
+        public static Tensor kullback_leibler_divergence(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor poisson(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor cosine_similarity(Tensor y_true, Tensor y_pred, int axis = -1) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Metrics/AUC.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class AUC
+    public class AUC : Metric
     {
+        public AUC(int num_thresholds= 200, string curve= "ROC", string summation_method= "interpolation",
+                    string name= null, string dtype= null, float thresholds= 0.5f,
+                    bool multi_label= false, Tensor label_weights= null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        private void _build(TensorShape shape) => throw new NotImplementedException();
+
+        public Tensor interpolate_pr_auc() => throw new NotImplementedException();
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Accuracy.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Accuracy
+    public class Accuracy : MeanMetricWrapper
     {
+        public Accuracy(string name = "accuracy", string dtype = null)
+            : base(Metric.accuracy, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/BinaryAccuracy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class BinaryAccuracy
+    public class BinaryAccuracy : MeanMetricWrapper
     {
+        public BinaryAccuracy(string name = "binary_accuracy", string dtype = null, float threshold = 0.5f)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.binary_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CategoricalAccuracy.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CategoricalAccuracy
+    public class CategoricalAccuracy : MeanMetricWrapper
     {
+        public CategoricalAccuracy(string name = "categorical_accuracy", string dtype = null)
+            : base(Metric.categorical_accuracy, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CategoricalHinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CategoricalHinge
+    public class CategoricalHinge : MeanMetricWrapper
     {
+        public CategoricalHinge(string name = "categorical_hinge", string dtype = null)
+            : base(Losses.Loss.categorical_hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CosineSimilarity.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CosineSimilarity
+    public class CosineSimilarity : MeanMetricWrapper
     {
+        public CosineSimilarity(string name = "cosine_similarity", string dtype = null, int axis = -1)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.cosine_proximity(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/FalseNegatives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class FalseNegatives
+    public class FalseNegatives : _ConfusionMatrixConditionCount
     {
+        public FalseNegatives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.FALSE_NEGATIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/FalsePositives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class FalsePositives
+    public class FalsePositives : _ConfusionMatrixConditionCount
     {
+        public FalsePositives(float thresholds = 0.5F, string name = null, string dtype = null) 
+            : base(Utils.MetricsUtils.ConfusionMatrix.FALSE_POSITIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Hinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Hinge
+    public class Hinge : MeanMetricWrapper
     {
+        public Hinge(string name = "hinge", string dtype = null)
+            : base(Losses.Loss.hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/LogCoshError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class LogCoshError
+    public class LogCoshError : MeanMetricWrapper
     {
+        public LogCoshError(string name = "logcosh", string dtype = null)
+            : base(Losses.Loss.logcosh, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Mean.cs

@@ -4,7 +4,12 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Mean
+    public class Mean : Reduce
     {
+        public Mean(string name, string dtype = null)
+           : base(Reduction.MEAN, name, dtype)
+        {
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanAbsoluteError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanAbsoluteError
+    public class MeanAbsoluteError : MeanMetricWrapper
     {
+        public MeanAbsoluteError(string name = "mean_absolute_error", string dtype = null)
+            : base(Losses.Loss.mean_absolute_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanAbsolutePercentageError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanAbsolutePercentageError
+    public class MeanAbsolutePercentageError : MeanMetricWrapper
     {
+        public MeanAbsolutePercentageError(string name = "mean_absolute_percentage_error", string dtype = null)
+            : base(Losses.Loss.mean_absolute_percentage_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanMetricWrapper.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanMetricWrapper
+    public class MeanMetricWrapper : Mean
     {
+        public MeanMetricWrapper(Func<Tensor, Tensor, Tensor> fn, string name, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanRelativeError.cs

@@ -1,10 +1,30 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanRelativeError
+    public class MeanRelativeError : Metric
     {
+        public MeanRelativeError(Tensor normalizer, string name, string dtype) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanSquaredError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanSquaredError
+    public class MeanSquaredError : MeanMetricWrapper
     {
+        public MeanSquaredError(string name = "mean_squared_error", string dtype = null)
+            : base(Losses.Loss.mean_squared_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanSquaredLogarithmicError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanSquaredLogarithmicError
+    public class MeanSquaredLogarithmicError : MeanMetricWrapper
     {
+        public MeanSquaredLogarithmicError(string name = "mean_squared_logarithmic_error", string dtype = null)
+            : base(Losses.Loss.mean_squared_logarithmic_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Metric.cs

@@ -35,5 +35,29 @@ namespace Tensorflow.Keras.Metrics
         public void add_weight(string name, TensorShape shape= null, VariableAggregation aggregation= VariableAggregation.Sum,
                                 VariableSynchronization synchronization = VariableSynchronization.OnRead, Initializers.Initializer initializer= null, 
                                 string dtype= null) => throw new NotImplementedException();
+
+        public static Tensor accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor binary_accuracy(Tensor y_true, Tensor y_pred, float threshold = 0.5f) => throw new NotImplementedException();
+
+        public static Tensor categorical_accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor sparse_categorical_accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor top_k_categorical_accuracy(Tensor y_true, Tensor y_pred, int k = 5) => throw new NotImplementedException();
+
+        public static Tensor sparse_top_k_categorical_accuracy(Tensor y_true, Tensor y_pred, int k = 5) => throw new NotImplementedException();
+
+        public static Tensor cosine_proximity(Tensor y_true, Tensor y_pred, int axis = -1) => throw new NotImplementedException();
+
+        public static Metric clone_metric(Metric metric) => throw new NotImplementedException();
+
+        public static Metric[] clone_metrics(Metric[] metric) => throw new NotImplementedException();
+
+        public static string serialize(Metric metric) => throw new NotImplementedException();
+
+        public static Metric deserialize(string config, object custom_objects = null) => throw new NotImplementedException();
+
+        public static Metric get(object identifier) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Metrics/Poisson.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Poisson
+    public class Poisson : MeanMetricWrapper
     {
+        public Poisson(string name = "logcosh", string dtype = null)
+            : base(Losses.Loss.logcosh, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Precision.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Precision
+    public class Precision : Metric
     {
+        public Precision(float? thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public Precision(float[] thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/PrecisionAtRecall.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class PrecisionAtRecall
+    public class PrecisionAtRecall : SensitivitySpecificityBase
     {
+        public PrecisionAtRecall(float recall, int num_thresholds = 200, string name = null, string dtype = null) : base(recall, num_thresholds, name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Recall.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Recall
+    public class Recall : Metric
     {
+        public Recall(float? thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public Recall(float[] thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/Reduce.cs

@@ -4,7 +4,22 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Reduce
+    public class Reduce : Metric
     {
+        public Reduce(string reduction, string name, string dtype= null)
+            : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/RootMeanSquaredError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class RootMeanSquaredError
+    public class RootMeanSquaredError : Mean
     {
+        public RootMeanSquaredError(string name = "root_mean_squared_error", string dtype = null)
+            : base(name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SensitivityAtSpecificity.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SensitivityAtSpecificity
+    public class SensitivityAtSpecificity : SensitivitySpecificityBase
     {
+        public SensitivityAtSpecificity(float specificity, int num_thresholds = 200, string name = null, string dtype = null) : base(specificity, num_thresholds, name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SensitivitySpecificityBase.cs

@@ -4,7 +4,26 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SensitivitySpecificityBase
+    public class SensitivitySpecificityBase : Metric
     {
+        public SensitivitySpecificityBase(float value, int num_thresholds= 200, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SparseCategoricalAccuracy.cs

@@ -4,7 +4,12 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SparseCategoricalAccuracy
+    public class SparseCategoricalAccuracy : MeanMetricWrapper
     {
+        public SparseCategoricalAccuracy(string name = "sparse_categorical_accuracy", string dtype = null)
+            : base(Metric.sparse_categorical_accuracy, name, dtype)
+        {
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/SparseTopKCategoricalAccuracy.cs

@@ -4,7 +4,17 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SparseTopKCategoricalAccuracy
+    public class SparseTopKCategoricalAccuracy : MeanMetricWrapper
     {
+        public SparseTopKCategoricalAccuracy(int k = 5, string name = "sparse_top_k_categorical_accuracy", string dtype = null)
+            : base(Fn, name, dtype)
+        {
+            
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.sparse_top_k_categorical_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SquaredHinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SquaredHinge
+    public class SquaredHinge : MeanMetricWrapper
     {
+        public SquaredHinge(string name = "squared_hinge", string dtype = null)
+            : base(Losses.Loss.squared_hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Sum.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Sum
+    public class Sum : Reduce
     {
+        public Sum(string name, string dtype = null)
+           : base(Reduction.SUM, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TopKCategoricalAccuracy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TopKCategoricalAccuracy
+    public class TopKCategoricalAccuracy : MeanMetricWrapper
     {
+        public TopKCategoricalAccuracy(int k = 5, string name = "top_k_categorical_accuracy", string dtype = null)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.top_k_categorical_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TrueNegatives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TrueNegatives
+    public class TrueNegatives : _ConfusionMatrixConditionCount
     {
+        public TrueNegatives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.TRUE_NEGATIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TruePositives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TruePositives
+    public class TruePositives : _ConfusionMatrixConditionCount
     {
+        public TruePositives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.TRUE_POSITIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/_ConfusionMatrixConditionCount.cs

@@ -1,10 +1,37 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
+using static Tensorflow.Keras.Utils.MetricsUtils;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class _ConfusionMatrixConditionCount
+    public class _ConfusionMatrixConditionCount : Metric
     {
+        public _ConfusionMatrixConditionCount(string confusion_matrix_cond, float thresholds= 0.5f, string name= null, string dtype= null)
+            : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Optimizer/Optimizer.cs

@@ -28,7 +28,7 @@ namespace Tensorflow.Keras
 
         public static string serialize(Optimizer optimizer) => throw new NotImplementedException();
 
-        public static string deserialize(string config, object custom_objects = null) => throw new NotImplementedException();
+        public static Optimizer deserialize(string config, object custom_objects = null) => throw new NotImplementedException();
 
         public static Optimizer get(object identifier) => throw new NotImplementedException();