diff --git a/src/TensorFlowNET.Core/Tensors/Tensors.cs b/src/TensorFlowNET.Core/Tensors/Tensors.cs
index cba8f9541..259b1eec7 100644
--- a/src/TensorFlowNET.Core/Tensors/Tensors.cs
+++ b/src/TensorFlowNET.Core/Tensors/Tensors.cs
@@ -58,17 +58,12 @@ public Tensor? SingleOrNull
         public Tensor this[params string[] slices]
             => this.First()[slices];
 
-        public Tensors(Tensor tensor) : base(tensor)
-        {
-
-        }
-
         private Tensors(Nest<Tensor> nested) : base(nested)
         {
 
         }
 
-        public Tensors(params Tensor[] tensors): base(tensors.Select(x => new Nest<Tensor>(x)))
+        public Tensors(params Tensor[] tensors): base(DealWithConstructorArrayInput(tensors))
         {
             
         }
@@ -83,6 +78,22 @@ public Tensors(NDArray nd): base(ops.convert_to_tensor(nd))
             
         }
 
+        private static Nest<Tensor> DealWithConstructorArrayInput(Tensor[] tensors)
+        {
+            if (tensors.Length == 0)
+            {
+                return Nest<Tensor>.Empty;
+            }
+            else if(tensors.Length == 1)
+            {
+                return new Nest<Tensor>(tensors[0]);
+            }
+            else
+            {
+                return new Nest<Tensor>(tensors.Select(x => new Nest<Tensor>(x)));
+            }
+        }
+
         public bool IsSingle()
         {
             return Length == 1;
@@ -107,9 +118,14 @@ public void Add(Tensor tensor)
                 ListValue = new() { new Nest<Tensor>(Value), new Nest<Tensor>(tensor) };
                 Value = null;
             }
-            else
+            else if(NestType == NestType.List)
+            {
+                ListValue!.Add(new Nest<Tensor>(tensor));
+            }
+            else //Empty
             {
-                ListValue.Add(new Nest<Tensor>(tensor));
+                NestType = NestType.Node;
+                Value = tensor;
             }
         }
 
@@ -128,9 +144,14 @@ public void AddRange(IEnumerable<Tensor> tensors)
                 ListValue.AddRange(tensors.Select(x => new Nest<Tensor>(x)));
                 Value = null;
             }
-            else
+            else if(NestType == NestType.List)
             {
-                ListValue.AddRange(tensors.Select(x => new Nest<Tensor>(x)));
+                ListValue!.AddRange(tensors.Select(x => new Nest<Tensor>(x)));
+            }
+            else // empty
+            {
+                NestType = NestType.List;
+                ListValue = tensors.Select(x => new Nest<Tensor>(x)).ToList();
             }
         }
 
diff --git a/src/TensorFlowNET.Keras/BackendImpl.cs b/src/TensorFlowNET.Keras/BackendImpl.cs
index 30b73e82f..144910669 100644
--- a/src/TensorFlowNET.Keras/BackendImpl.cs
+++ b/src/TensorFlowNET.Keras/BackendImpl.cs
@@ -651,13 +651,13 @@ object _get_input_tensor(int time)
                         states = Nest.PackSequenceAs(states, flat_final_states).ToTensors();
                         if (return_all_outputs)
                         {
-                            successive_outputs.Add(output);
-                            successive_states.Add(states);
+                            successive_outputs = successive_outputs.MergeWith(output);
+                            successive_outputs = successive_states.MergeWith(states);
                         }
                         else
                         {
-                            successive_outputs = new Tensors { output };
-                            successive_states = new Tensors { states };
+                            successive_outputs = new Tensors(output);
+                            successive_states = new Tensors(states);
                         }
 
                     }
@@ -722,16 +722,11 @@ object _get_input_tensor(int time)
                 // Get the time(0) input and compute the output for that, the output will
                 // be used to determine the dtype of output tensor array. Don't read from
                 // input_ta due to TensorArray clear_after_read default to True.
-                var inps = new Tensors();
-                foreach (var inp in flatted_inptus)
-                {
-                    inps.Add(inp[0]);
-                }
-                var input_time_zero = Nest.PackSequenceAs(inputs, inps).ToTensors();
+                var input_time_zero = Nest.PackSequenceAs(inputs, flatted_inptus.Select(x => x[0]).ToArray()).ToTensors();
 
                 // output_time_zero is used to determine the cell output shape and its
                 // dtype.  the value is discarded.
-                (output_time_zero, _) = step_function((Tensor)input_time_zero, 
+                (output_time_zero, _) = step_function(input_time_zero, 
                     constants is null ? initial_states : initial_states.MergeWith(constants));
 
                 int output_ta_size = return_all_outputs ? time_steps_t : 1;
@@ -816,6 +811,7 @@ object _get_input_tensor(int time)
 
                 Func<Tensor, Tensor> cond = (time) => (time < time_steps_t);
                 int parallel_iterations = 32;
+                new_states = states;
                 if (masking_fn != null)
                 {
                     // Mask for the T output will be base on the output of T - 1. In the
@@ -846,7 +842,7 @@ RNN step function.
                         // TODO(Wanglongzhi2001),deal with nest.pack_sequence_as's return type
                         var current_input = Nest.PackSequenceAs(inputs, flat_current_input).ToTensors();
                         var mask_t = masking_fn(time);
-                        var (output, new_states_internal) = step_function(current_input, states.MergeWith(constants));
+                        var (output, new_states_internal) = step_function(current_input, new_states.MergeWith(constants));
                         // mask output
                         var flat_output = Nest.Flatten(output).ToList();
 
@@ -871,11 +867,12 @@ RNN step function.
                         new_states_internal = Nest.PackSequenceAs(new_states, flat_final_state).ToTensors();
 
                         var ta_index_to_write = return_all_outputs ? time : tf.constant(0);
-                        // TODO(Wanglongzhi2001),deal with zip output_ta_t
-                        foreach (var (ta, Out) in zip(output_ta_t, flat_new_output))
+                        output_ta_t = zip(output_ta_t, flat_new_output).Select(item =>
                         {
-                            output_ta_t.Add(ta.write(ta_index_to_write, Out));
-                        }
+                            var (ta, out_) = item;
+                            return ta.write(ta_index_to_write, out_);
+                        }).ToList();
+
 
                         new_states_internal = Nest.PackSequenceAs(initial_states, flat_new_state).ToTensors();
 
@@ -921,15 +918,8 @@ Tensor _step(Tensor time)
                     }
                     var final_outputs = tf.while_loop(cond: cond, body: _step, loop_vars: time, parallel_iterations: parallel_iterations);
                 }
-                //Tensors outputs = new Tensors();
-                foreach (var o in output_ta)
-                {
-                    outputs.Add(o.stack());
-                }
-                foreach (var o in outputs)
-                {
-                    last_output.Add(o[-1]);
-                }
+                outputs = outputs.MergeWith(output_ta.Select(o => o.stack()).ToTensors());
+                last_output = last_output.MergeWith(outputs.Select(o => o[-1]).ToTensors());
                 outputs = Nest.PackSequenceAs(output_time_zero, outputs).ToTensors();
                 last_output = Nest.PackSequenceAs(output_time_zero, last_output).ToTensors();