return array instead of tuple for layer.call

src/TensorFlowNET.Core/Keras/Layers/BatchNormalization.cs

@@ -139,14 +139,14 @@ namespace Tensorflow.Keras.Layers
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected override Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             Tensor outputs = null;
 
             if (fused)
             {
                 outputs = _fused_batch_norm(inputs, training: training);
-                return (outputs, outputs);
+                return new[] { outputs, outputs };
             }
 
             throw new NotImplementedException("BatchNormalization call");

src/TensorFlowNET.Core/Keras/Layers/Conv.cs

@@ -108,7 +108,7 @@ namespace Tensorflow.Keras.Layers
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected override Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             var outputs = _convolution_op.__call__(inputs, kernel);
             if (use_bias)
@@ -126,7 +126,7 @@ namespace Tensorflow.Keras.Layers
             if (activation != null)
                 outputs = activation.Activate(outputs);
 
-            return (outputs, outputs);
+            return new[] { outputs, outputs };
         }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Dense.cs

@@ -72,7 +72,7 @@ namespace Tensorflow.Keras.Layers
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected override Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             Tensor outputs = null;
             var rank = inputs.rank;
@@ -90,7 +90,7 @@ namespace Tensorflow.Keras.Layers
             if (activation != null)
                 outputs = activation.Activate(outputs);
 
-            return (outputs, outputs);
+            return new[] { outputs, outputs };
         }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Embedding.cs

@@ -50,14 +50,14 @@ namespace Tensorflow.Keras.Layers
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected override Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             var dtype = inputs.dtype;
             if (dtype != tf.int32 && dtype != tf.int64)
                 inputs = math_ops.cast(inputs, tf.int32);
 
             var @out = embedding_ops.embedding_lookup(embeddings, inputs);
-            return (@out, @out);
+            return new[] { @out, @out };
         }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Layer.cs

@@ -103,14 +103,14 @@ namespace Tensorflow.Keras.Layers
             _inbound_nodes = new List<Node>();
         }
 
-        public (Tensor, Tensor) __call__(Tensor[] inputs,
+        public Tensor[] __call__(Tensor[] inputs,
             Tensor training = null,
             Tensor state = null,
             VariableScope scope = null)
         {
             var input_list = inputs;
             var input = inputs[0];
-            Tensor outputs = null;
+            Tensor[] outputs = null;
 
             // We will attempt to build a TF graph if & only if all inputs are symbolic.
             // This is always the case in graph mode. It can also be the case in eager
@@ -142,25 +142,26 @@ namespace Tensorflow.Keras.Layers
                     // overridden).
                     _maybe_build(inputs[0]);
 
-                    (input, outputs) = call(inputs[0], 
+                    outputs = call(inputs[0], 
                         training: training,
                         state: state);
+
                     (input, outputs) = _set_connectivity_metadata_(input, outputs);
                     _handle_activity_regularization(inputs[0], outputs);
                     _set_mask_metadata(inputs[0], outputs, null);
                 });
             }
 
-            return (input, outputs);
+            return outputs;
         }
 
-        private (Tensor, Tensor) _set_connectivity_metadata_(Tensor inputs, Tensor outputs)
+        private (Tensor, Tensor[]) _set_connectivity_metadata_(Tensor inputs, Tensor[] outputs)
         {
             //_add_inbound_node(input_tensors: inputs, output_tensors: outputs);
             return (inputs, outputs);
         }
 
-        private void _handle_activity_regularization(Tensor inputs, Tensor outputs)
+        private void _handle_activity_regularization(Tensor inputs, Tensor[] outputs)
         {
             //if(_activity_regularizer != null)
             {
@@ -168,7 +169,7 @@ namespace Tensorflow.Keras.Layers
             }
         }
 
-        private void _set_mask_metadata(Tensor inputs, Tensor outputs, Tensor previous_mask)
+        private void _set_mask_metadata(Tensor inputs, Tensor[] outputs, Tensor previous_mask)
         {
 
         }
@@ -178,9 +179,9 @@ namespace Tensorflow.Keras.Layers
             return null;
         }
 
-        protected virtual (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected virtual Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
-            return (inputs, inputs);
+            throw new NotImplementedException("");
         }
 
         protected virtual string _name_scope()

src/TensorFlowNET.Core/Keras/Layers/Pooling2D.cs

@@ -43,7 +43,7 @@ namespace Tensorflow.Keras.Layers
             this.input_spec = new InputSpec(ndim: 4);
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
+        protected override Tensor[] call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             int[] pool_shape;
             if (data_format == "channels_last")
@@ -64,7 +64,7 @@ namespace Tensorflow.Keras.Layers
                 padding: padding.ToUpper(),
                 data_format: conv_utils.convert_data_format(data_format, 4));
 
-            return (outputs, outputs);
+            return new[] { outputs, outputs };
         }
     }
 }