simplify with function.

src/TensorFlowNET.Core/Framework/importer.py.cs

@@ -7,7 +7,7 @@ using static Tensorflow.OpDef.Types;
 
 namespace Tensorflow
 {
-    public class importer
+    public class importer : Python
     {
         public static ITensorOrOperation[] import_graph_def(GraphDef graph_def,
             Dictionary<string, Tensor> input_map = null,
@@ -26,7 +26,7 @@ namespace Tensorflow
 
             string prefix = "";
             var graph = ops.get_default_graph();
-            Python.with<ops.name_scope>(new ops.name_scope(name, "import", input_map.Values), scope =>
+            with(new ops.name_scope(name, "import", input_map.Values), scope =>
             {
                 prefix = scope;
                 /*if (!string.IsNullOrEmpty(prefix))

src/TensorFlowNET.Core/Gradients/gradients_impl.py.cs

@@ -7,7 +7,7 @@ using System.Threading;
 
 namespace Tensorflow
 {
-    public class gradients_impl
+    public class gradients_impl : Python
     {
         public static Tensor[] gradients(Tensor[] ys,
             Tensor[] xs, 
@@ -58,7 +58,7 @@ namespace Tensorflow
              **/
             var grads = new Dictionary<string, Tensor[][]>();
 
-            Python.with<ops.name_scope>(new ops.name_scope(name, "gradients", values: all), scope =>
+            with(new ops.name_scope(name, "gradients", values: all), scope =>
             {
                 string grad_scope = scope;
                 // Get a uid for this call to gradients that can be used to help
@@ -131,7 +131,7 @@ namespace Tensorflow
                             // for ops that do not have gradients.
                             var grad_fn = ops.get_gradient_function(op);
 
-                            Python.with<ops.name_scope>(new ops.name_scope(op.name + "_grad"), scope1 =>
+                            with(new ops.name_scope(op.name + "_grad"), scope1 =>
                             {
                                 string name1 = scope1;
                                 if (grad_fn != null)

src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
             string scope = "",
             string loss_collection= "losses")
         {
-            with<ops.name_scope>(new ops.name_scope(scope,
+            with(new ops.name_scope(scope,
                 "sparse_softmax_cross_entropy_loss",
                 (logits, labels, weights)),
                 namescope =>

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -10,7 +10,7 @@ using static Tensorflow.OpDef.Types;
 
 namespace Tensorflow
 {
-    public class OpDefLibrary
+    public class OpDefLibrary : Python
     {
         public Operation _apply_op_helper(string op_type_name, string name = null, dynamic args = null)
         {
@@ -44,7 +44,7 @@ namespace Tensorflow
             var input_types = new List<TF_DataType>();
             dynamic values = null;
 
-            return Python.with<ops.name_scope, Operation>(new ops.name_scope(name), scope =>
+            return with(new ops.name_scope(name), scope =>
             {
                 var inferred_from = new Dictionary<string, object>();
                 var base_types = new List<TF_DataType>();

src/TensorFlowNET.Core/Operations/array_ops.py.cs

@@ -5,14 +5,14 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class array_ops
+    public class array_ops : Python
     {
         public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = null) => gen_array_ops.placeholder_with_default(input, shape, name);
 
         public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "zeros", shape), scope =>
+            return with(new ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -68,7 +68,7 @@ namespace Tensorflow
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "ones_like", new { tensor }), scope =>
+            return with(new ops.name_scope(name, "ones_like", new { tensor }), scope =>
             {
                 name = scope;
                 var tensor1 = ops.convert_to_tensor(tensor, name: "tensor");
@@ -84,7 +84,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "ones", new { shape }), scope =>
+            return with(new ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = gen_array_ops.fill(shape, constant_op.constant(1.0f, dtype: dtype), name: name);
@@ -130,7 +130,7 @@ namespace Tensorflow
 
         private static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Shape", new { input }), scope =>
+            return with(new ops.name_scope(name, "Shape", new { input }), scope =>
             {
                 name = scope;
 
@@ -151,7 +151,7 @@ namespace Tensorflow
 
         private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Size", new Tensor[] { input }), scope =>
+            return with(new ops.name_scope(name, "Size", new Tensor[] { input }), scope =>
             {
                 name = scope;
 
@@ -182,7 +182,7 @@ namespace Tensorflow
 
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
+            return with(new ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
             {
                 name = scope;
                 tensor = ops.convert_to_tensor(tensor, name: "tensor");

src/TensorFlowNET.Core/Operations/control_flow_ops.py.cs

@@ -9,7 +9,7 @@ namespace Tensorflow
     {
         public static Operation group<T>(T[] inputs, string name = null) where T : ITensorOrOperation
         {
-            return with<ops.name_scope, Operation>(new ops.name_scope(name, "group_deps", inputs), scope =>
+            return with(new ops.name_scope(name, "group_deps", inputs), scope =>
             {
                 name = scope;
 
@@ -39,7 +39,7 @@ namespace Tensorflow
 
         private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = null)
         {
-            return Python.with<_ControlDependenciesController, Operation>(ops.control_dependencies(deps), ctl =>
+            return with(ops.control_dependencies(deps), ctl =>
             {
                 if (dev == null)
                 {
@@ -83,7 +83,7 @@ namespace Tensorflow
 
         public static Tensor[] tuple(Tensor[] tensors, string name = null, Operation[] control_inputs = null)
         {
-            return Python.with<ops.name_scope, Tensor[]>(new ops.name_scope(name, "tuple", tensors), scope =>
+            return with(new ops.name_scope(name, "tuple", tensors), scope =>
             {
                 name = scope;
                 var gating_ops = tensors.Select(x => x.op).ToList();
@@ -115,11 +115,11 @@ namespace Tensorflow
             values.AddRange(dependencies);
             values.Add(output_tensor);
 
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "control_dependency", values), scope =>
+            return with(new ops.name_scope(name, "control_dependency", values), scope =>
             {
                 name = scope;
 
-                return Python.with<_ControlDependenciesController, Tensor>(ops.control_dependencies(dependencies), ctl =>
+                return with(ops.control_dependencies(dependencies), ctl =>
                 {
                     output_tensor = ops.convert_to_tensor_or_composite(output_tensor);
                     return _Identity(output_tensor, name: name);

src/TensorFlowNET.Core/Operations/math_ops.py.cs

@@ -14,7 +14,7 @@ namespace Tensorflow
             if(base_type == x.dtype)
                 return x;
 
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Cast", new { x }), scope =>
+            return with(new ops.name_scope(name, "Cast", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.as_base_dtype() != base_type)
@@ -141,7 +141,7 @@ namespace Tensorflow
             if (delta == null)
                 delta = 1;
 
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Range", new object[] { start, limit, delta }), scope =>
+            return with(new ops.name_scope(name, "Range", new object[] { start, limit, delta }), scope =>
             {
                 name = scope;
                 var start1 = ops.convert_to_tensor(start, name: "start");
@@ -154,7 +154,7 @@ namespace Tensorflow
 
         public static Tensor floordiv(Tensor x, Tensor y, string name = null)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "floordiv", new { x, y }), scope =>
+            return with(new ops.name_scope(name, "floordiv", new { x, y }), scope =>
             {
                 return gen_math_ops.floor_div(x, y, scope);
             });
@@ -162,7 +162,7 @@ namespace Tensorflow
 
         public static Tensor rank_internal(Tensor input, string name = null, bool optimize = true)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
+            return with(new ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
             {
                 name = scope;
                 var input_tensor = ops.convert_to_tensor(input);
@@ -182,7 +182,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            Python.with<ops.name_scope>(new ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
+            with(new ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
             {
                 name = scope;
 
@@ -212,7 +212,7 @@ namespace Tensorflow
             if (dt.is_floating() || dt.is_integer())
                 return x;
 
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
+            return with(new ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
             {
 
                 return x;

src/TensorFlowNET.Core/Operations/nn_impl.py.cs

@@ -20,7 +20,7 @@ namespace Tensorflow
             bool keep_dims = false)
         {
             Tuple<Tensor, Tensor> t = null;
-            with<ops.name_scope>(new ops.name_scope(name, "moments", new { x, axes }), scope =>
+            with(new ops.name_scope(name, "moments", new { x, axes }), scope =>
             {
                 // The dynamic range of fp16 is too limited to support the collection of
                 // sufficient statistics. As a workaround we simply perform the operations

src/TensorFlowNET.Core/Operations/random_ops.py.cs

@@ -23,7 +23,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "random_normal", new object[] { shape, mean, stddev }), scope =>
+            return with(new ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
             {
                 var shape_tensor = _ShapeTensor(shape);
                 var mean_tensor = ops.convert_to_tensor(mean, dtype: dtype, name: "mean");
@@ -53,7 +53,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            return with(new ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
                 var tensorShape = _ShapeTensor(shape);

src/TensorFlowNET.Core/Python.cs

@@ -43,12 +43,12 @@ namespace Tensorflow
             }
         }
 
-        public static void with<T>(IPython py, Action<T> action) where T : IPython
+        public static void with<T>(T py, Action<T> action) where T : IPython
         {
             try
             {
                 py.__enter__();
-                action((T)py);
+                action(py);
             }
             catch (Exception ex)
             {
@@ -62,12 +62,12 @@ namespace Tensorflow
             }
         }
 
-        public static TOut with<TIn, TOut>(IPython py, Func<TIn, TOut> action) where TIn : IPython
+        public static TOut with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
         {
             try
             {
                 py.__enter__();
-                return action((TIn)py);
+                return action(py);
             }
             catch (Exception ex)
             {

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
                 dtype = tr.dtype.as_base_dtype();
             
             var namescope = new ops.name_scope(null, name, new { x, y });
-            return Python.with<ops.name_scope, Tensor>(namescope, scope =>
+            return with(namescope, scope =>
             {
                 Tensor result = null;
                 var x1 = ops.convert_to_tensor(x, dtype: dtype, name: "x");

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
     /// A tensor is a generalization of vectors and matrices to potentially higher dimensions. 
     /// Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes.
     /// </summary>
-    public partial class Tensor : IDisposable, ITensorOrOperation
+    public partial class Tensor : Python, IDisposable, ITensorOrOperation
     {
         private readonly IntPtr _handle;
 

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
     /// class directly, but instead instantiate one of its subclasses such as
     /// `GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.
     /// </summary>
-    public abstract class Optimizer
+    public abstract class Optimizer : Python
     {
         // Values for gate_gradients.
         public static int GATE_NONE = 0;
@@ -87,7 +87,7 @@ namespace Tensorflow
             _create_slots(var_list);
 
             var update_ops = new List<Operation>();
-            return Python.with<ops.name_scope, Operation>(new ops.name_scope(name, Name), scope =>
+            return with(new ops.name_scope(name, Name), scope =>
             {
                 name = scope;
                 _prepare();
@@ -98,7 +98,7 @@ namespace Tensorflow
                         continue;
 
                     var scope_name = var.op.name;
-                    Python.with<ops.name_scope>(new ops.name_scope("update_" + scope_name), scope2 =>
+                    with(new ops.name_scope("update_" + scope_name), scope2 =>
                     {
                         update_ops.Add(processor.update_op(this, grad));
                     });

src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs

@@ -5,7 +5,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class BaseSaverBuilder
+    public class BaseSaverBuilder : Python
     {
         protected SaverDef.Types.CheckpointFormatVersion _write_version;
 
@@ -79,7 +79,7 @@ namespace Tensorflow
             Tensor save_tensor = null;
             Operation restore_op = null;
 
-            return Python.with<ops.name_scope, SaverDef>(new ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
+            return with(new ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs

@@ -17,7 +17,7 @@ namespace Tensorflow
         private static Tensor op_helper<T>(string default_name, RefVariable x, T y)
         {
             var tensor1 = x.value();
-            return with<ops.name_scope, Tensor>(new ops.name_scope(null, default_name, new { tensor1, y }), scope => {
+            return with(new ops.name_scope(null, default_name, new { tensor1, y }), scope => {
                 var tensor2 = ops.convert_to_tensor(y, tensor1.dtype.as_base_dtype(), "y");
                 return gen_math_ops.add(tensor1, tensor2, scope);
             });

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -118,7 +118,7 @@ namespace Tensorflow
 
             ops.init_scope();
             var values = init_from_fn ? new object[0] : new object[] { initial_value };
-            with<ops.name_scope>(new ops.name_scope(name, "Variable", values), scope =>
+            with(new ops.name_scope(name, "Variable", values), scope =>
             {
                 name = scope;
                 if (init_from_fn)
@@ -132,7 +132,7 @@ namespace Tensorflow
                         List = new AttrValue.Types.ListValue()
                     };
                     attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
-                    with<ops.name_scope>(new ops.name_scope("Initializer"), scope2 =>
+                    with(new ops.name_scope("Initializer"), scope2 =>
                     {
                         _initial_value = (initial_value as Func<Tensor>)();
                         _initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -7,7 +7,7 @@ namespace Tensorflow
     /// <summary>
     /// Variable scope object to carry defaults to provide to `get_variable`
     /// </summary>
-    public class VariableScope
+    public class VariableScope : Python
     {
         public bool use_resource { get; set; }
         private _ReuseMode _reuse;
@@ -38,7 +38,7 @@ namespace Tensorflow
             VariableAggregation aggregation= VariableAggregation.NONE)
         {
             string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
-            return Python.with<ops.name_scope, RefVariable>(new ops.name_scope(null), scope =>
+            return with(new ops.name_scope(null), scope =>
             {
                 if (dtype == TF_DataType.DtInvalid)
                     dtype = _dtype;

src/TensorFlowNET.Core/ops.py.cs

@@ -12,7 +12,7 @@ using System.ComponentModel;
 
 namespace Tensorflow
 {
-    public partial class ops
+    public partial class ops : Python
     {
         public static void add_to_collection<T>(string name, T value)
         {
@@ -216,7 +216,7 @@ namespace Tensorflow
             // inner_device_stack = default_graph._device_function_stack
             // var outer_context = default_graph.as_default;
 
-            Python.with(ops.control_dependencies(null), delegate
+            with(ops.control_dependencies(null), delegate
             {
                 var outer_graph = get_default_graph();
                 // outer_device_stack = None

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -39,7 +39,7 @@ namespace TensorFlowNET.Examples
 
                 var idx = 0;
                 float propability = 0;
-                with<Session>(tf.Session(graph), sess =>
+                with(tf.Session(graph), sess =>
                 {
                     var results = sess.run(output_operation.outputs[0], new FeedItem(input_operation.outputs[0], tensor));
                     var probabilities = results.Data<float>();
@@ -63,7 +63,7 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 117,
                                 int input_std = 1)
         {
-            return with<Graph, NDArray>(tf.Graph().as_default(), graph =>
+            return with(tf.Graph().as_default(), graph =>
             {
                 var file_reader = tf.read_file(file_name, "file_reader");
                 var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
@@ -74,7 +74,7 @@ namespace TensorFlowNET.Examples
                 var sub = tf.subtract(bilinear, new float[] { input_mean });
                 var normalized = tf.divide(sub, new float[] { input_std });
 
-                return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));
+                return with(tf.Session(graph), sess => sess.run(normalized));
             });
         }
 

test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs

@@ -46,7 +46,7 @@ namespace TensorFlowNET.Examples
             var input_operation = graph.get_operation_by_name(input_name);
             var output_operation = graph.get_operation_by_name(output_name);
 
-            var results = with<Session, NDArray>(tf.Session(graph),
+            var results = with(tf.Session(graph),
                 sess => sess.run(output_operation.outputs[0],
                     new FeedItem(input_operation.outputs[0], nd)));
 
@@ -68,7 +68,7 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 0,
                                 int input_std = 255)
         {
-            return with<Graph, NDArray>(tf.Graph().as_default(), graph =>
+            return with(tf.Graph().as_default(), graph =>
             {
                 var file_reader = tf.read_file(file_name, "file_reader");
                 var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
@@ -79,7 +79,7 @@ namespace TensorFlowNET.Examples
                 var sub = tf.subtract(bilinear, new float[] { input_mean });
                 var normalized = tf.divide(sub, new float[] { input_std });
 
-                return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));
+                return with(tf.Session(graph), sess => sess.run(normalized));
             });
         }
 

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -53,7 +53,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
 
             // Start training
-            with<Session>(tf.Session(), sess => 
+            with(tf.Session(), sess => 
             {
                 // Run the initializer
                 sess.run(init);

test/TensorFlowNET.Examples/MetaGraph.cs

@@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples
 
         private void ImportMetaGraph(string dir)
         {
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta");
                 new_saver.restore(sess, dir + "my-model-10000");

test/TensorFlowNET.UnitTest/CApiTest.cs

@@ -6,7 +6,7 @@ using Tensorflow;
 
 namespace TensorFlowNET.UnitTest
 {
-    public class CApiTest
+    public class CApiTest : Python
     {
         protected TF_Code TF_OK = TF_Code.TF_OK;
         protected TF_DataType TF_FLOAT = TF_DataType.TF_FLOAT;

test/TensorFlowNET.UnitTest/ConstantTest.cs

@@ -10,7 +10,7 @@ using Tensorflow;
 namespace TensorFlowNET.UnitTest
 {
     [TestClass]
-    public class ConstantTest
+    public class ConstantTest : Python
     {
         Status status = new Status();
 
@@ -27,7 +27,7 @@ namespace TensorFlowNET.UnitTest
         {
             string str = "Hello, TensorFlow.NET!";
             var tensor = tf.constant(str);
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
                 Assert.IsTrue(result.Data<string>()[0] == str);
@@ -39,7 +39,7 @@ namespace TensorFlowNET.UnitTest
         {
             // small size
             var tensor = tf.zeros(new Shape(3, 2), TF_DataType.TF_INT32, "small");
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
 
@@ -50,7 +50,7 @@ namespace TensorFlowNET.UnitTest
 
             // big size
             tensor = tf.zeros(new Shape(200, 100), TF_DataType.TF_INT32, "big");
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
 
@@ -74,7 +74,7 @@ namespace TensorFlowNET.UnitTest
             });
 
             var tensor = tf.constant(nd);
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
                 var data = result.Data<int>();

test/TensorFlowNET.UnitTest/NameScopeTest.cs

@@ -15,7 +15,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void NestedNameScope()
         {
-            with<ops.name_scope>(new ops.name_scope("scope1"), scope1 =>
+            with(new ops.name_scope("scope1"), scope1 =>
             {
                 name = scope1;
                 Assert.AreEqual("scope1", g._name_stack);
@@ -24,7 +24,7 @@ namespace TensorFlowNET.UnitTest
                 var const1 = tf.constant(1.0);
                 Assert.AreEqual("scope1/Const:0", const1.name);
 
-                with<ops.name_scope>(new ops.name_scope("scope2"), scope2 =>
+                with(new ops.name_scope("scope2"), scope2 =>
                 {
                     name = scope2;
                     Assert.AreEqual("scope1/scope2", g._name_stack);

test/TensorFlowNET.UnitTest/PlaceholderTest.cs

@@ -7,7 +7,7 @@ using Tensorflow;
 namespace TensorFlowNET.UnitTest
 {
     [TestClass]
-    public class PlaceholderTest
+    public class PlaceholderTest : Python
     {
         [TestMethod]
         public void placeholder()
@@ -15,7 +15,7 @@ namespace TensorFlowNET.UnitTest
             var x = tf.placeholder(tf.int32);
             var y = x * 3;
 
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(y, 
                     new FeedItem(x, 2));

test/TensorFlowNET.UnitTest/SessionTest.cs

@@ -82,7 +82,7 @@ namespace TensorFlowNET.UnitTest
             var a = constant_op.constant(np.array(3.0).reshape(1, 1));
             var b = constant_op.constant(np.array(2.0).reshape(1, 1));
             var c = math_ops.matmul(a, b, name: "matmul");
-            Python.with(tf.Session(), delegate
+            with(tf.Session(), delegate
             {
                 var result = c.eval();
                 Assert.AreEqual(6, result.Data<double>()[0]);

test/TensorFlowNET.UnitTest/TrainSaverTest.cs

@@ -19,7 +19,7 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportGraph()
         {
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta");
             });
@@ -44,7 +44,7 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportSavedModel()
         {
-            with<Session>(Session.LoadFromSavedModel("mobilenet"), sess =>
+            with(Session.LoadFromSavedModel("mobilenet"), sess =>
             {
                 
             });
@@ -65,7 +65,7 @@ namespace TensorFlowNET.UnitTest
             // Add ops to save and restore all the variables.
             var saver = tf.train.Saver();
 
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 sess.run(init_op);
 

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -32,9 +32,10 @@ namespace TensorFlowNET.UnitTest
 
         /// <summary>
         /// https://www.tensorflow.org/api_docs/python/tf/variable_scope
+        /// how to create a new variable
         /// </summary>
         [TestMethod]
-        public void VarCreation1()
+        public void VarCreation()
         {
             with(tf.variable_scope("foo"), delegate
             {
@@ -46,6 +47,12 @@ namespace TensorFlowNET.UnitTest
             });
         }
 
+        [TestMethod]
+        public void ReenterVariableScope()
+        {
+
+        }
+
         [TestMethod]
         public void ScalarVar()
         {
@@ -65,7 +72,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Assign1()
         {
-            with<Graph>(tf.Graph().as_default(), graph =>
+            with(tf.Graph().as_default(), graph =>
             {
                 var variable = tf.Variable(31, name: "tree");
                 var init = tf.global_variables_initializer();
@@ -91,7 +98,7 @@ namespace TensorFlowNET.UnitTest
             // Add an op to initialize the variables.
             var init_op = tf.global_variables_initializer();
 
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 sess.run(init_op);
                 // o some work with the model.