diff --git a/src/Native/LibTorchSharp/THSConvolution.cpp b/src/Native/LibTorchSharp/THSConvolution.cpp
index e1500d939..fb51e4c91 100644
--- a/src/Native/LibTorchSharp/THSConvolution.cpp
+++ b/src/Native/LibTorchSharp/THSConvolution.cpp
@@ -408,218 +408,6 @@ Tensor THSNN_FractionalMaxPool3d_forward_with_indices(const NNModule module, con
     return ResultTensor(std::get<0>(res));
 }
 
-NNModule THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ZeroPad2dOptions(padding);
-        res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ZeroPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });
-    res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ZeroPad2d>()->forward(*tensor));
-}
-
-NNModule THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad1dOptions(padding, value);
-        res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ConstantPad1d>()->forward(*tensor));
-}
-
-NNModule THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad2dOptions(padding, value);
-    res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ConstantPad2d>()->forward(*tensor));
-}
-
-NNModule THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad3dOptions(padding, value);
-    res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ConstantPad3d>()->forward(*tensor));
-}
-
-NNModule THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad1dOptions({ padding_left, padding_right }, value);
-    res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom }, value);
-    res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ConstantPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back }, value);
-    res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad1dOptions(padding);
-    res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad1d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad2dOptions(padding);
-    res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad2d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad3dOptions(padding);
-    res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-
-Tensor   THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad3d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad1dOptions({ padding_left, padding_right });
-    res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });
-    res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReplicationPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });
-    res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad1dOptions(padding);
-    res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad1d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad2dOptions(padding);
-    res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad2d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad3dOptions(padding);
-    res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor   THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad3d>()->forward(*tensor));
-}
-
-NNModule THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad1dOptions({ padding_left, padding_right });
-    res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });
-    res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);
-    );
-}
-
-NNModule THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::ReflectionPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });
-    res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);
-    );
-}
-
-
 template<typename T>
 void ApplyPaddingMode(T& opts, const int64_t padding)
 {
diff --git a/src/Native/LibTorchSharp/THSNN.cpp b/src/Native/LibTorchSharp/THSNN.cpp
index 6f9ca1742..b522c6a74 100644
--- a/src/Native/LibTorchSharp/THSNN.cpp
+++ b/src/Native/LibTorchSharp/THSNN.cpp
@@ -16,40 +16,6 @@ Tensor THSNN_Identity_forward(const NNModule module, const Tensor tensor)
     CATCH_TENSOR((*module)->as<torch::nn::Identity>()->forward(*tensor));
 }
 
-NNModule THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool bias,
-    NNAnyModule* outAsAnyModule)
-{
-    CATCH_RETURN_NNModule(
-        auto opts = torch::nn::LinearOptions(input_size, output_size).bias(bias);
-        res = create_module<torch::nn::LinearImpl>(opts, outAsAnyModule);
-    );
-}
-
-Tensor THSNN_Linear_forward(const NNModule module, const Tensor tensor)
-{
-    CATCH_TENSOR((*module)->as<torch::nn::Linear>()->forward(*tensor));
-}
-
-Tensor THSNN_Linear_bias(const NNModule module)
-{
-    return get_bias<torch::nn::Linear>(module);
-}
-
-void THSNN_Linear_set_bias(const NNModule module, const Tensor bias)
-{
-    set_bias<torch::nn::Linear>(module, bias);
-}
-
-Tensor THSNN_Linear_weight(const NNModule module)
-{
-    return get_weight<torch::nn::Linear>(module);
-}
-
-void THSNN_Linear_set_weight(const NNModule module, const Tensor weight)
-{
-    set_weight<torch::nn::Linear>(module, weight);
-}
-
 Tensor THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias)
 {
     CATCH_TENSOR(bias == nullptr ?
diff --git a/src/Native/LibTorchSharp/THSNN.h b/src/Native/LibTorchSharp/THSNN.h
index 330ceb1d2..7d7b3fc0f 100644
--- a/src/Native/LibTorchSharp/THSNN.h
+++ b/src/Native/LibTorchSharp/THSNN.h
@@ -94,42 +94,6 @@ EXPORT_API(Tensor)   THSNN_LPPool1d_forward(const NNModule module, const Tensor
 EXPORT_API(NNModule) THSNN_LPPool2d_ctor(double norm_type, const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, bool ceil_mode, NNAnyModule* outAsAnyModule);
 EXPORT_API(Tensor)   THSNN_LPPool2d_forward(const NNModule module, const Tensor tensor);
 
-// Padding
-
-EXPORT_API(NNModule) THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor);
-
-EXPORT_API(NNModule) THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor);
-
-EXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor);
-
-EXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);
-EXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor);
-
 // Convolution
 
 EXPORT_API(NNModule) THSNN_Conv1d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);
@@ -325,12 +289,6 @@ EXPORT_API(Tensor) THSNN_unfold(const Tensor input, const int64_t kernel1, const
 
 EXPORT_API(NNModule) THSNN_Identity_ctor(NNAnyModule* outAsAnyModule);
 EXPORT_API(Tensor)   THSNN_Identity_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(NNModule) THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool with_bias, NNAnyModule* outAsAnyModule);
-EXPORT_API(Tensor)   THSNN_Linear_forward(const NNModule module, const Tensor tensor);
-EXPORT_API(Tensor)   THSNN_Linear_bias(const NNModule module);
-EXPORT_API(void)     THSNN_Linear_set_bias(const NNModule module, const Tensor tensor);
-EXPORT_API(Tensor)   THSNN_Linear_weight(const NNModule module);
-EXPORT_API(void)     THSNN_Linear_set_weight(const NNModule module, const Tensor tensor);
 
 EXPORT_API(Tensor) THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias);
 EXPORT_API(Tensor) THSNN_functional_bilinear(const Tensor input1, const Tensor input2, const Tensor weights, const Tensor bias);
diff --git a/src/TorchSharp/NN/Activation/CELU.cs b/src/TorchSharp/NN/Activation/CELU.cs
index 0af37f6b6..85ba92195 100644
--- a/src/TorchSharp/NN/Activation/CELU.cs
+++ b/src/TorchSharp/NN/Activation/CELU.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/ELU.cs b/src/TorchSharp/NN/Activation/ELU.cs
index 365280f87..4dc818b0a 100644
--- a/src/TorchSharp/NN/Activation/ELU.cs
+++ b/src/TorchSharp/NN/Activation/ELU.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/GELU.cs b/src/TorchSharp/NN/Activation/GELU.cs
index 71dda326e..c069ca639 100644
--- a/src/TorchSharp/NN/Activation/GELU.cs
+++ b/src/TorchSharp/NN/Activation/GELU.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/GLU.cs b/src/TorchSharp/NN/Activation/GLU.cs
index 15e717272..95ca2c25d 100644
--- a/src/TorchSharp/NN/Activation/GLU.cs
+++ b/src/TorchSharp/NN/Activation/GLU.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/Hardshrink.cs b/src/TorchSharp/NN/Activation/Hardshrink.cs
index b3f2684d3..c4f880b72 100644
--- a/src/TorchSharp/NN/Activation/Hardshrink.cs
+++ b/src/TorchSharp/NN/Activation/Hardshrink.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/Hardsigmoid.cs b/src/TorchSharp/NN/Activation/Hardsigmoid.cs
index c4f354cf2..66e0aa816 100644
--- a/src/TorchSharp/NN/Activation/Hardsigmoid.cs
+++ b/src/TorchSharp/NN/Activation/Hardsigmoid.cs
@@ -33,7 +33,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/Hardswish.cs b/src/TorchSharp/NN/Activation/Hardswish.cs
index 6f8b401da..d1065be3b 100644
--- a/src/TorchSharp/NN/Activation/Hardswish.cs
+++ b/src/TorchSharp/NN/Activation/Hardswish.cs
@@ -33,7 +33,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Activation/Hardtanh.cs b/src/TorchSharp/NN/Activation/Hardtanh.cs
index f7236a72a..9451c2132 100644
--- a/src/TorchSharp/NN/Activation/Hardtanh.cs
+++ b/src/TorchSharp/NN/Activation/Hardtanh.cs
@@ -31,7 +31,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Bilinear.cs b/src/TorchSharp/NN/Bilinear.cs
index 316f81ea0..17a9dc903 100644
--- a/src/TorchSharp/NN/Bilinear.cs
+++ b/src/TorchSharp/NN/Bilinear.cs
@@ -13,46 +13,57 @@ namespace Modules
     {
         public sealed class Bilinear : Module<Tensor, Tensor, Tensor>
         {
-            internal Bilinear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            public new static Bilinear Load(string modelPath)
+            internal Bilinear(long in1_features, long in2_features, long out_features, bool hasBias = true, Device? device = null, ScalarType? dtype = null) : base(nameof(Bilinear))
             {
-                var res = Module<Tensor, Tensor>.Load(modelPath);
-                return new Bilinear(res.handle.DangerousGetHandle(), IntPtr.Zero);
+                weight = torch.empty(out_features, in1_features, in2_features, device: device, dtype: dtype).AsParameter();
+                var bound = 1 / Math.Sqrt(weight!.shape[1]);
+
+                init.uniform_(_weight, -bound, bound);
+
+                if (hasBias) {
+                    bias = torch.empty(out_features, device: device, dtype: dtype).AsParameter();
+                    var (fanIn, _) = init.CalculateFanInAndFanOut(weight);
+                    init.uniform_(_bias, -bound, bound);
+                }
+                //NOTE: it's important not to call 'RegisterComponents' here.
             }
 
             public override Tensor forward(Tensor input1, Tensor input2)
             {
-                var res = THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle);
-                if (res == IntPtr.Zero) { CheckForErrors(); }
-                return new Tensor(res);
+                return torch.nn.functional.bilinear(input1, input2, _weight!, _bias);
             }
 
-            public Parameter? bias {
-                get {
-                    var res = THSNN_Bilinear_bias(handle);
-                    if (res == IntPtr.Zero) { CheckForErrors(); }
-                    return ((res == IntPtr.Zero) ? null : new Parameter(res));
+            protected override void Dispose(bool disposing)
+            {
+                if (disposing) {
+                    _weight?.Dispose();
+                    _bias?.Dispose();
                 }
+            }
+
+            public Parameter? bias {
+                get => _bias;
                 set {
-                    THSNN_Bilinear_set_bias(handle, value?.Handle ?? IntPtr.Zero);
-                    CheckForErrors();
-                    ConditionallyRegisterParameter("bias", value);
+                    _bias?.Dispose();
+                    _bias = value?.DetachFromDisposeScope() as Parameter;
+                    ConditionallyRegisterParameter(nameof(bias), _bias);
                 }
             }
+            private Parameter? _bias;
 
-            public Parameter? weight {
-                get {
-                    var res = THSNN_Bilinear_weight(handle);
-                    if (res == IntPtr.Zero) { CheckForErrors(); }
-                    return (res == IntPtr.Zero) ? null : new Parameter(res);
-                }
+            public Parameter weight {
+                get => _weight!;
                 set {
-                    THSNN_Bilinear_set_weight(handle, value?.Handle ?? IntPtr.Zero);
-                    CheckForErrors();
-                    ConditionallyRegisterParameter("weight", value);
+                    if (value is null) throw new ArgumentNullException(nameof(weight));
+                    if (value.Handle != _weight?.Handle) {
+                        _weight?.Dispose();
+                        _weight = (value.DetachFromDisposeScope() as Parameter)!;
+                        ConditionallyRegisterParameter(nameof(weight), _weight);
+                    }
                 }
             }
+
+            private Parameter? _weight;
         }
     }
 
@@ -64,19 +75,16 @@ public static partial class nn
             /// <summary>
             /// Applies a bilinear transformation to the incoming data
             /// </summary>
-            /// <param name="in1Features">size of each first input sample</param>
-            /// <param name="in2Features">size of each second input sample</param>
-            /// <param name="outputSize">size of each output sample</param>
+            /// <param name="in1_features">size of each first input sample</param>
+            /// <param name="in2_features">size of each second input sample</param>
+            /// <param name="out_features">size of each output sample</param>
             /// <param name="hasBias">If set to false, the layer will not learn an additive bias</param>
             /// <param name="device">The desired device of the parameters and buffers in this module</param>
             /// <param name="dtype">The desired floating point or complex dtype of the parameters and buffers in this module</param>
             /// <returns></returns>
-            public static Bilinear Bilinear(long in1Features, long in2Features, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)
+            public static Bilinear Bilinear(long in1_features, long in2_features, long out_features, bool hasBias = true, Device? device = null, ScalarType? dtype = null)
             {
-                var res = THSNN_Bilinear_ctor(in1Features, in2Features, outputSize, hasBias, out var boxedHandle);
-                if (res == IntPtr.Zero) { CheckForErrors(); }
-
-                return new Bilinear(res, boxedHandle).MoveModule<Bilinear>(device, dtype);
+                return new Bilinear(in1_features, in2_features, out_features, hasBias, device, dtype);
             }
 
             public static partial class functional
@@ -94,7 +102,7 @@ public static Tensor bilinear(Tensor input1, Tensor input2, Tensor weight, Tenso
                 {
                     IntPtr bPtr = bias?.Handle ?? IntPtr.Zero;
                     var res = THSNN_functional_bilinear(input1.Handle, input2.Handle, weight.Handle, bPtr);
-                    if (res == IntPtr.Zero) { CheckForErrors(); }
+                    if (res == IntPtr.Zero) { torch.CheckForErrors(); }
                     return new Tensor(res);
                 }
             }
diff --git a/src/TorchSharp/NN/CosineSimilarity.cs b/src/TorchSharp/NN/CosineSimilarity.cs
index 261565e03..31323384d 100644
--- a/src/TorchSharp/NN/CosineSimilarity.cs
+++ b/src/TorchSharp/NN/CosineSimilarity.cs
@@ -24,6 +24,14 @@ public override Tensor forward(Tensor input1, Tensor input2)
                 if (res == IntPtr.Zero) { torch.CheckForErrors(); }
                 return new Tensor(res);
             }
+
+            // Rather than spending cycles only to discover that this module has neither
+            // parameters nor buffers, just shortcut the move completely.
+            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
+            protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
 
diff --git a/src/TorchSharp/NN/Dropout.cs b/src/TorchSharp/NN/Dropout.cs
index b2d31dbae..f1c3a04b0 100644
--- a/src/TorchSharp/NN/Dropout.cs
+++ b/src/TorchSharp/NN/Dropout.cs
@@ -12,7 +12,7 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a dropout module.
         /// </summary>
-        public sealed class Dropout : torch.nn.Module<Tensor, Tensor>
+        public sealed class Dropout : ParamLessModule<Tensor, Tensor>
         {
             internal Dropout(double p = 0.5, bool inplace = false) : base(nameof(Dropout))
             {
diff --git a/src/TorchSharp/NN/Flatten.cs b/src/TorchSharp/NN/Flatten.cs
index b1568938c..e6bf6ccc9 100644
--- a/src/TorchSharp/NN/Flatten.cs
+++ b/src/TorchSharp/NN/Flatten.cs
@@ -10,21 +10,24 @@ namespace TorchSharp
     namespace Modules
     {
         /// <summary>
-        /// This class is used to represent a dropout module for 2d/3d convolutational layers.
+        /// This class is used to represent a flattening of the input tensors.
         /// </summary>
-        public sealed class Flatten : torch.nn.Module<Tensor, Tensor>
+        public sealed class Flatten : ParamLessModule<Tensor, Tensor>
         {
-            internal Flatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)
+            internal Flatten(long startDim = 1, long endDim = -1) : base(nameof(Flatten))
             {
+                _startDim = startDim;
+                _endDim = endDim;
             }
 
             public override Tensor forward(Tensor tensor)
             {
-                var res = THSNN_Flatten_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
+                return tensor.flatten(_startDim, _endDim);
             }
 
+            private long _startDim;
+            private long _endDim;
+
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
@@ -40,14 +43,12 @@ public static partial class nn
             /// <summary>
             /// Flattens a contiguous range of dims into a tensor. For use with Sequential.
             /// </summary>
-            /// <param name="startDim">First dim to flatten (default = 1).</param>
-            /// <param name="endDim">Last dim to flatten (default = -1).</param>
+            /// <param name="start_dim">First dim to flatten (default = 1).</param>
+            /// <param name="end_dim">Last dim to flatten (default = -1).</param>
             /// <returns></returns>
-            public static Flatten Flatten(long startDim = 1, long endDim = -1)
+            public static Flatten Flatten(long start_dim = 1, long end_dim = -1)
             {
-                var handle = THSNN_Flatten_ctor(startDim, endDim, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Flatten(handle, boxedHandle);
+                return new Flatten(start_dim, end_dim);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Identity.cs b/src/TorchSharp/NN/Identity.cs
index 7296a52a1..97bd68098 100644
--- a/src/TorchSharp/NN/Identity.cs
+++ b/src/TorchSharp/NN/Identity.cs
@@ -10,15 +10,13 @@ namespace TorchSharp
 
     namespace Modules
     {
-        public sealed class Identity : torch.nn.Module<Tensor, Tensor>
+        public sealed class Identity : ParamLessModule<Tensor, Tensor>
         {
-            internal Identity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
+            internal Identity() : base(nameof(Identity)) { }
 
             public override Tensor forward(Tensor tensor)
             {
-                var res = THSNN_Identity_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
+                return tensor.alias();
             }
 
             // Rather than spending cycles only to discover that this module has neither
@@ -39,9 +37,7 @@ public static partial class nn
             /// <returns>The same tensor as is input.</returns>
             public static Identity Identity()
             {
-                var res = THSNN_Identity_ctor(out var boxedHandle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Identity(res, boxedHandle);
+                return new Identity();
             }
         }
     }
diff --git a/src/TorchSharp/NN/Linear.cs b/src/TorchSharp/NN/Linear.cs
index b5aa0c653..1b87c4a3a 100644
--- a/src/TorchSharp/NN/Linear.cs
+++ b/src/TorchSharp/NN/Linear.cs
@@ -13,48 +13,57 @@ namespace Modules
     {
         public sealed class Linear : torch.nn.Module<Tensor, Tensor>
         {
-            internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)
+            internal Linear(long inputSize, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null) : base(nameof(Linear))
             {
+                weight = torch.empty(outputSize, inputSize, device: device, dtype: dtype).AsParameter();
+                init.kaiming_uniform_(weight, a: _sqrt5);
+
+                if (hasBias) {
+                    bias = torch.empty(outputSize, device: device, dtype: dtype).AsParameter();
+                    var (fanIn, _) = init.CalculateFanInAndFanOut(weight);
+                    var bound = fanIn > 0 ? 1 / Math.Sqrt(fanIn) : 0;
+                    init.uniform_(_bias, -bound, bound);
+                }
+                //NOTE: it's important not to call 'RegisterComponents' here.
             }
 
-            public new static Linear Load(string modelPath)
+            public override Tensor forward(Tensor tensor)
             {
-                var res = Module<Tensor, Tensor>.Load(modelPath);
-                return new Linear(res.handle.DangerousGetHandle(), IntPtr.Zero);
+                return torch.nn.functional.linear(tensor, _weight!, _bias);
             }
 
-            public override Tensor forward(Tensor tensor)
+            protected override void Dispose(bool disposing)
             {
-                var res = THSNN_Linear_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
+                if (disposing) {
+                    _weight?.Dispose();
+                    _bias?.Dispose();
+                }
             }
 
             public Parameter? bias {
-                get {
-                    var res = THSNN_Linear_bias(handle);
-                    if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                    return ((res == IntPtr.Zero) ? null : new Parameter(res));
-                }
+                get => _bias;
                 set {
-                    THSNN_Linear_set_bias(handle, value?.Handle ?? IntPtr.Zero);
-                    torch.CheckForErrors();
-                    ConditionallyRegisterParameter("bias", value);
+                    _bias?.Dispose();
+                    _bias = value?.DetachFromDisposeScope() as Parameter;
+                    ConditionallyRegisterParameter(nameof(bias), _bias);
                 }
             }
+            private Parameter? _bias;
 
-            public Parameter? weight {
-                get {
-                    var res = THSNN_Linear_weight(handle);
-                    if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                    return (res == IntPtr.Zero) ? null : new Parameter(res);
-                }
+            public Parameter weight {
+                get => _weight!;
                 set {
-                    THSNN_Linear_set_weight(handle, value!.Handle);
-                    torch.CheckForErrors();
-                    ConditionallyRegisterParameter("weight", value);
+                    if (value is null) throw new ArgumentNullException(nameof(weight));
+                    if (value.Handle != _weight?.Handle) {
+                        _weight?.Dispose();
+                        _weight = (value.DetachFromDisposeScope() as Parameter)!;
+                        ConditionallyRegisterParameter(nameof(weight), _weight);
+                    }
                 }
             }
+
+            private Parameter? _weight;
+            private static readonly double _sqrt5 = Math.Sqrt(5);
         }
     }
 
@@ -72,10 +81,7 @@ public static partial class nn
             /// <param name="dtype">The desired floating point or complex dtype of the parameters and buffers in this module</param>
             public static Linear Linear(long inputSize, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)
             {
-                var res = THSNN_Linear_ctor(inputSize, outputSize, hasBias, out var boxedHandle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-
-                return new Linear(res, boxedHandle).MoveModule<Linear>(device, dtype);
+                return new Linear(inputSize, outputSize, hasBias, device, dtype);
             }
 
             public static partial class functional
diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs
index d423f05c2..2cf86411b 100644
--- a/src/TorchSharp/NN/Module.cs
+++ b/src/TorchSharp/NN/Module.cs
@@ -126,10 +126,10 @@ protected virtual void Dispose(bool disposing)
                     if (disposing && !handle.IsInvalid) {
 
                         foreach (var (_, p) in named_buffers(false)) {
-                            p.Dispose();
+                            p.DetachFromDisposeScope().Dispose();
                         }
                         foreach (var (_, b) in named_parameters(false)) {
-                            b.Dispose();
+                            b.DetachFromDisposeScope().Dispose();
                         }
 
                         foreach (var (_, m) in named_modules()) {
diff --git a/src/TorchSharp/NN/Padding/ConstantPad1d.cs b/src/TorchSharp/NN/Padding/ConstantPad1d.cs
index ad6771e7b..ec905b4b7 100644
--- a/src/TorchSharp/NN/Padding/ConstantPad1d.cs
+++ b/src/TorchSharp/NN/Padding/ConstantPad1d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ConstantPad1d module.
         /// </summary>
-        public sealed class ConstantPad1d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ConstantPad1d : PadBase
         {
-            internal ConstantPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ConstantPad1d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ConstantPad1d(double value, params long[] padding) : base(nameof(ConstantPad1d), PaddingModes.Constant, value, padding) { }
         }
     }
 
@@ -48,9 +30,7 @@ public static partial class nn
             /// <returns></returns>
             public static ConstantPad1d ConstantPad1d(long padding, double value)
             {
-                var handle = THSNN_ConstantPad1d_ctor(value, padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad1d(handle, boxedHandle);
+                return new ConstantPad1d(value, padding, padding);
             }
 
             /// <summary>
@@ -61,9 +41,7 @@ public static ConstantPad1d ConstantPad1d(long padding, double value)
             /// <returns></returns>
             public static ConstantPad1d ConstantPad1d((long, long) padding, double value)
             {
-                var handle = THSNN_ConstantPad1d_ctor_tuple(value, padding.Item1, padding.Item2, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad1d(handle, boxedHandle);
+                return new ConstantPad1d(value, padding.Item1, padding.Item2);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ConstantPad2d.cs b/src/TorchSharp/NN/Padding/ConstantPad2d.cs
index 7d54b7bc6..9bc47b2be 100644
--- a/src/TorchSharp/NN/Padding/ConstantPad2d.cs
+++ b/src/TorchSharp/NN/Padding/ConstantPad2d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ConstantPad2d module.
         /// </summary>
-        public sealed class ConstantPad2d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ConstantPad2d : PadBase
         {
-            internal ConstantPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ConstantPad2d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ConstantPad2d(double value, params long[] padding) : base(nameof(ConstantPad2d), PaddingModes.Constant, value, padding) { }
         }
     }
 
@@ -48,9 +30,7 @@ public static partial class nn
             /// <returns></returns>
             public static ConstantPad2d ConstantPad2d(long padding, double value)
             {
-                var handle = THSNN_ConstantPad2d_ctor(value, padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad2d(handle, boxedHandle);
+                return new ConstantPad2d(value, padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -61,9 +41,7 @@ public static ConstantPad2d ConstantPad2d(long padding, double value)
             /// <returns></returns>
             public static ConstantPad2d ConstantPad2d((long, long, long, long) padding, double value)
             {
-                var handle = THSNN_ConstantPad2d_ctor_tuple(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad2d(handle, boxedHandle);
+                return new ConstantPad2d(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ConstantPad3d.cs b/src/TorchSharp/NN/Padding/ConstantPad3d.cs
index 4ab2c55fb..4da9344e0 100644
--- a/src/TorchSharp/NN/Padding/ConstantPad3d.cs
+++ b/src/TorchSharp/NN/Padding/ConstantPad3d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ConstantPad3d module.
         /// </summary>
-        public sealed class ConstantPad3d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ConstantPad3d : PadBase
         {
-            internal ConstantPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ConstantPad3d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ConstantPad3d(double value, params long[] padding) : base(nameof(ConstantPad3d), PaddingModes.Constant, value, padding) { }
         }
     }
 
@@ -48,9 +30,7 @@ public static partial class nn
             /// <returns></returns>
             public static ConstantPad3d ConstantPad3d(long padding, double value)
             {
-                var handle = THSNN_ConstantPad3d_ctor(value, padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad3d(handle, boxedHandle);
+                return new ConstantPad3d(value, padding, padding, padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -61,9 +41,7 @@ public static ConstantPad3d ConstantPad3d(long padding, double value)
             /// <returns></returns>
             public static ConstantPad3d ConstantPad3d((long, long, long, long, long, long) padding, double value)
             {
-                var handle = THSNN_ConstantPad3d_ctor_tuple(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ConstantPad3d(handle, boxedHandle);
+                return new ConstantPad3d(value, padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/PadBase.cs b/src/TorchSharp/NN/Padding/PadBase.cs
new file mode 100644
index 000000000..20c0dde47
--- /dev/null
+++ b/src/TorchSharp/NN/Padding/PadBase.cs
@@ -0,0 +1,39 @@
+// Copyright (c) .NET Foundation and Contributors.  All Rights Reserved.  See LICENSE in the project root for license information.
+using System;
+using static TorchSharp.torch;
+using static TorchSharp.PInvoke.NativeMethods;
+
+namespace TorchSharp
+{
+    using Modules;
+
+    namespace Modules
+    {
+        /// <summary>
+        /// This class is used to represent the base of all padding-related modules.
+        /// </summary>
+        public class PadBase : ParamLessModule<Tensor, Tensor>
+        {
+            protected PadBase(string name, PaddingModes mode, double value, params long[] padding) : base(name)
+            {
+                _value = value;
+                _padding = padding;
+                _paddingMode = mode;
+            }
+
+            /// <summary>
+            /// Forward pass.
+            /// </summary>
+            /// <param name="input">Input tensor</param>
+            /// <returns></returns>
+            public override Tensor forward(Tensor input)
+            {
+                return nn.functional.pad(input, _padding, _paddingMode, _value);
+            }
+
+            private PaddingModes _paddingMode;
+            private long[] _padding;
+            private double _value = 0.0;
+        }
+    }
+}
diff --git a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs
index 1a975dd7d..780f77550 100644
--- a/src/TorchSharp/NN/Padding/ReflectionPad1d.cs
+++ b/src/TorchSharp/NN/Padding/ReflectionPad1d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReflectionPad1d module.
         /// </summary>
-        public sealed class ReflectionPad1d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReflectionPad1d : PadBase
         {
-            internal ReflectionPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReflectionPad1d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReflectionPad1d(params long[] padding) : base(nameof(ReflectionPad1d), PaddingModes.Reflect, 0, padding) { }
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ReflectionPad1d ReflectionPad1d(long padding)
             {
-                var handle = THSNN_ReflectionPad1d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad1d(handle, boxedHandle);
+                return new ReflectionPad1d(padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReflectionPad1d ReflectionPad1d(long padding)
             /// <returns></returns>
             public static ReflectionPad1d ReflectionPad1d((long, long) padding)
             {
-                var handle = THSNN_ReflectionPad1d_ctor_tuple(padding.Item1, padding.Item2, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad1d(handle, boxedHandle);
+                return new ReflectionPad1d(padding.Item1, padding.Item2);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs
index 418e971c3..1aa15f2e8 100644
--- a/src/TorchSharp/NN/Padding/ReflectionPad2d.cs
+++ b/src/TorchSharp/NN/Padding/ReflectionPad2d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReflectionPad2d module.
         /// </summary>
-        public sealed class ReflectionPad2d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReflectionPad2d : PadBase
         {
-            internal ReflectionPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReflectionPad2d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReflectionPad2d(params long[] padding) : base(nameof(ReflectionPad1d), PaddingModes.Reflect, 0, padding) { }        
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ReflectionPad2d ReflectionPad2d(long padding)
             {
-                var handle = THSNN_ReflectionPad2d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad2d(handle, boxedHandle);
+                return new ReflectionPad2d(padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReflectionPad2d ReflectionPad2d(long padding)
             /// <returns></returns>
             public static ReflectionPad2d ReflectionPad2d((long, long, long, long) padding)
             {
-                var handle = THSNN_ReflectionPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad2d(handle, boxedHandle);
+                return new ReflectionPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs
index 18db464be..cf26874ff 100644
--- a/src/TorchSharp/NN/Padding/ReflectionPad3d.cs
+++ b/src/TorchSharp/NN/Padding/ReflectionPad3d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReflectionPad3d module.
         /// </summary>
-        public sealed class ReflectionPad3d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReflectionPad3d : PadBase
         {
-            internal ReflectionPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReflectionPad3d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReflectionPad3d(params long[] padding) : base(nameof(ReflectionPad1d), PaddingModes.Reflect, 0, padding) { }
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ReflectionPad3d ReflectionPad3d(long padding)
             {
-                var handle = THSNN_ReflectionPad3d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad3d(handle, boxedHandle);
+                return new ReflectionPad3d(padding, padding, padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReflectionPad3d ReflectionPad3d(long padding)
             /// <returns></returns>
             public static ReflectionPad3d ReflectionPad3d((long, long, long, long, long, long) padding)
             {
-                var handle = THSNN_ReflectionPad3d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReflectionPad3d(handle, boxedHandle);
+                return new ReflectionPad3d(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs
index 55f572ee8..fb3744f5b 100644
--- a/src/TorchSharp/NN/Padding/ReplicationPad1d.cs
+++ b/src/TorchSharp/NN/Padding/ReplicationPad1d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReplicationPad1d module.
         /// </summary>
-        public sealed class ReplicationPad1d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReplicationPad1d : PadBase
         {
-            internal ReplicationPad1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReplicationPad1d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReplicationPad1d(params long[] padding) : base(nameof(ReplicationPad1d), PaddingModes.Replicate, 0, padding) { }
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ReplicationPad1d ReplicationPad1d(long padding)
             {
-                var handle = THSNN_ReplicationPad1d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad1d(handle, boxedHandle);
+                return new ReplicationPad1d(padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReplicationPad1d ReplicationPad1d(long padding)
             /// <returns></returns>
             public static ReplicationPad1d ReplicationPad1d((long, long) padding)
             {
-                var handle = THSNN_ReplicationPad1d_ctor_tuple(padding.Item1, padding.Item2, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad1d(handle, boxedHandle);
+                return new ReplicationPad1d(padding.Item1, padding.Item2);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs
index 205ac9e59..0bd779b0e 100644
--- a/src/TorchSharp/NN/Padding/ReplicationPad2d.cs
+++ b/src/TorchSharp/NN/Padding/ReplicationPad2d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReplicationPad2d module.
         /// </summary>
-        public sealed class ReplicationPad2d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReplicationPad2d : PadBase
         {
-            internal ReplicationPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReplicationPad2d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReplicationPad2d(params long[] padding) : base(nameof(ReplicationPad1d), PaddingModes.Replicate, 0, padding) { }
         }
     }
 
@@ -41,15 +23,13 @@ public static partial class torch
         public static partial class nn
         {
             /// <summary>
-            /// Pads the input tensor using replication of the input boundary.
+            /// Pads the input tensor using the replication of the input boundary.
             /// </summary>
             /// <param name="padding">The size of the padding.</param>
             /// <returns></returns>
             public static ReplicationPad2d ReplicationPad2d(long padding)
             {
-                var handle = THSNN_ReplicationPad2d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad2d(handle, boxedHandle);
+                return new ReplicationPad2d(padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReplicationPad2d ReplicationPad2d(long padding)
             /// <returns></returns>
             public static ReplicationPad2d ReplicationPad2d((long, long, long, long) padding)
             {
-                var handle = THSNN_ReplicationPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad2d(handle, boxedHandle);
+                return new ReplicationPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs
index 6b92f2972..5a243489e 100644
--- a/src/TorchSharp/NN/Padding/ReplicationPad3d.cs
+++ b/src/TorchSharp/NN/Padding/ReplicationPad3d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ReplicationPad3d module.
         /// </summary>
-        public sealed class ReplicationPad3d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ReplicationPad3d : PadBase
         {
-            internal ReplicationPad3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ReplicationPad3d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ReplicationPad3d(params long[] padding) : base(nameof(ReplicationPad1d), PaddingModes.Replicate, 0, padding) { }
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ReplicationPad3d ReplicationPad3d(long padding)
             {
-                var handle = THSNN_ReplicationPad3d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad3d(handle, boxedHandle);
+                return new ReplicationPad3d(padding, padding, padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ReplicationPad3d ReplicationPad3d(long padding)
             /// <returns></returns>
             public static ReplicationPad3d ReplicationPad3d((long, long, long, long, long, long) padding)
             {
-                var handle = THSNN_ReplicationPad3d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ReplicationPad3d(handle, boxedHandle);
+                return new ReplicationPad3d(padding.Item1, padding.Item2, padding.Item3, padding.Item4, padding.Item5, padding.Item6);
             }
         }
     }
diff --git a/src/TorchSharp/NN/Padding/ZeroPad2d.cs b/src/TorchSharp/NN/Padding/ZeroPad2d.cs
index 82a075d86..679e96e4d 100644
--- a/src/TorchSharp/NN/Padding/ZeroPad2d.cs
+++ b/src/TorchSharp/NN/Padding/ZeroPad2d.cs
@@ -12,27 +12,9 @@ namespace Modules
         /// <summary>
         /// This class is used to represent a ZeroPad2d module.
         /// </summary>
-        public sealed class ZeroPad2d : torch.nn.Module<Tensor, Tensor>
+        public sealed class ZeroPad2d : PadBase
         {
-            internal ZeroPad2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }
-
-            /// <summary>
-            /// Forward pass.
-            /// </summary>
-            /// <param name="tensor">Input tensor</param>
-            /// <returns></returns>
-            public override Tensor forward(Tensor tensor)
-            {
-                var res = THSNN_ZeroPad2d_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
-            }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            internal ZeroPad2d(params long[] padding) : base(nameof(ZeroPad2d), PaddingModes.Zeros, 0, padding) { }
         }
     }
 
@@ -47,9 +29,7 @@ public static partial class nn
             /// <returns></returns>
             public static ZeroPad2d ZeroPad2d(long padding)
             {
-                var handle = THSNN_ZeroPad2d_ctor(padding, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ZeroPad2d(handle, boxedHandle);
+                return new ZeroPad2d(padding, padding, padding, padding);
             }
 
             /// <summary>
@@ -59,9 +39,7 @@ public static ZeroPad2d ZeroPad2d(long padding)
             /// <returns></returns>
             public static ZeroPad2d ZeroPad2d((long, long, long, long) padding)
             {
-                var handle = THSNN_ZeroPad2d_ctor_tuple(padding.Item1, padding.Item2, padding.Item3, padding.Item4, out var boxedHandle);
-                if (handle == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new ZeroPad2d(handle, boxedHandle);
+                return new ZeroPad2d(padding.Item1, padding.Item2, padding.Item3, padding.Item4);
             }
         }
     }
diff --git a/src/TorchSharp/NN/ParamLessModule.cs b/src/TorchSharp/NN/ParamLessModule.cs
new file mode 100644
index 000000000..c2a274a52
--- /dev/null
+++ b/src/TorchSharp/NN/ParamLessModule.cs
@@ -0,0 +1,46 @@
+// Copyright (c) .NET Foundation and Contributors.  All Rights Reserved.  See LICENSE in the project root for license information.
+using System;
+using static TorchSharp.torch;
+using static TorchSharp.PInvoke.NativeMethods;
+
+namespace TorchSharp
+{
+    using Modules;
+
+    namespace Modules
+    {
+        /// <summary>
+        /// Base class for all modules that do not have any parameters or buffers, and
+        /// for which the `_to()` implementation can therefore be simplified.
+        /// </summary>
+        public abstract class ParamLessModule<T1, T2> : nn.Module<T1, T2>
+        {
+            protected ParamLessModule(string name) : base(name) { }
+
+            // Rather than spending cycles only to discover that this module has neither
+            // parameters nor buffers, just shortcut the move completely.
+            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
+            protected internal override nn.Module _to(ScalarType dtype) => this;
+        }
+
+        /// <summary>
+        /// Base class for all modules that do not have any parameters or buffers, and
+        /// for which the `_to()` implementation can therefore be simplified.
+        /// </summary>
+        public abstract class ParamLessModule<T1, T2, T3> : nn.Module<T1, T2, T3>
+        {
+            protected ParamLessModule(string name) : base(name) { }
+
+            // Rather than spending cycles only to discover that this module has neither
+            // parameters nor buffers, just shortcut the move completely.
+            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
+            protected internal override nn.Module _to(ScalarType dtype) => this;
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/TorchSharp/NN/Parameter.cs b/src/TorchSharp/NN/Parameter.cs
index 81e9051d8..4c1faa01e 100644
--- a/src/TorchSharp/NN/Parameter.cs
+++ b/src/TorchSharp/NN/Parameter.cs
@@ -26,6 +26,12 @@ public class Parameter : Tensor
             public Parameter(Tensor data, bool requires_grad = true) :
                 base(data.with_requires_grad(requires_grad).MoveHandle())
             {
+                var scope = data.OwningDisposeScope;
+                if (scope is not null) {
+                    this.OwningDisposeScope = scope;
+                    scope.Include(this);
+                    scope.Detach(data);
+                }
             }
 
             /// <summary>
@@ -35,7 +41,6 @@ public Parameter(Tensor data, bool requires_grad = true) :
             internal Parameter(System.IntPtr handle) : base(handle)
             {
             }
-
         };
     }
 
diff --git a/src/TorchSharp/NN/PixelShuffle.cs b/src/TorchSharp/NN/PixelShuffle.cs
index fe1d94bd5..e7054be2b 100644
--- a/src/TorchSharp/NN/PixelShuffle.cs
+++ b/src/TorchSharp/NN/PixelShuffle.cs
@@ -27,6 +27,14 @@ public override Tensor forward(Tensor tensor)
                 if (res == IntPtr.Zero) { torch.CheckForErrors(); }
                 return new Tensor(res);
             }
+
+            // Rather than spending cycles only to discover that this module has neither
+            // parameters nor buffers, just shortcut the move completely.
+            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
+            protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
 
diff --git a/src/TorchSharp/NN/PixelUnshuffle.cs b/src/TorchSharp/NN/PixelUnshuffle.cs
index e6d3f120a..5467ab59b 100644
--- a/src/TorchSharp/NN/PixelUnshuffle.cs
+++ b/src/TorchSharp/NN/PixelUnshuffle.cs
@@ -27,6 +27,14 @@ public override Tensor forward(Tensor tensor)
                 if (res == IntPtr.Zero) { torch.CheckForErrors(); }
                 return new Tensor(res);
             }
+
+            // Rather than spending cycles only to discover that this module has neither
+            // parameters nor buffers, just shortcut the move completely.
+            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
+            protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
 
diff --git a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs
index 7fbfa371a..169215cde 100644
--- a/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs
+++ b/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs
@@ -35,7 +35,9 @@ public override Tensor forward(Tensor tensor)
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/LPPool1d.cs b/src/TorchSharp/NN/Pooling/LPPool1d.cs
index 424da18d5..bd0c7d9bd 100644
--- a/src/TorchSharp/NN/Pooling/LPPool1d.cs
+++ b/src/TorchSharp/NN/Pooling/LPPool1d.cs
@@ -24,12 +24,6 @@ public override Tensor forward(Tensor tensor)
                 if (res == IntPtr.Zero) { torch.CheckForErrors(); }
                 return new Tensor(res);
             }
-
-            // Rather than spending cycles only to discover that this module has neither
-            // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-            protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
 
diff --git a/src/TorchSharp/NN/Pooling/LPPool2d.cs b/src/TorchSharp/NN/Pooling/LPPool2d.cs
index 67c06b58b..811d97598 100644
--- a/src/TorchSharp/NN/Pooling/LPPool2d.cs
+++ b/src/TorchSharp/NN/Pooling/LPPool2d.cs
@@ -28,7 +28,9 @@ public override Tensor forward(Tensor tensor)
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxPool1D.cs b/src/TorchSharp/NN/Pooling/MaxPool1D.cs
index 79a521f59..6484c149e 100644
--- a/src/TorchSharp/NN/Pooling/MaxPool1D.cs
+++ b/src/TorchSharp/NN/Pooling/MaxPool1D.cs
@@ -36,7 +36,9 @@ public override Tensor forward(Tensor tensor)
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxPool2D.cs b/src/TorchSharp/NN/Pooling/MaxPool2D.cs
index 55808c454..b8657ccb0 100644
--- a/src/TorchSharp/NN/Pooling/MaxPool2D.cs
+++ b/src/TorchSharp/NN/Pooling/MaxPool2D.cs
@@ -35,7 +35,9 @@ public override Tensor forward(Tensor tensor)
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxPool3D.cs b/src/TorchSharp/NN/Pooling/MaxPool3D.cs
index 1ab30d15d..165a0c847 100644
--- a/src/TorchSharp/NN/Pooling/MaxPool3D.cs
+++ b/src/TorchSharp/NN/Pooling/MaxPool3D.cs
@@ -36,7 +36,9 @@ public override Tensor forward(Tensor tensor)
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs
index 2d8d7e908..0f2fe9bd9 100644
--- a/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs
+++ b/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs
@@ -37,7 +37,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs
index 84e8c6cb3..ded7b81ee 100644
--- a/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs
+++ b/src/TorchSharp/NN/Pooling/MaxUnpool2d.cs
@@ -37,7 +37,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs
index a5473d8d6..32b85ce5a 100644
--- a/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs
+++ b/src/TorchSharp/NN/Pooling/MaxUnpool3d.cs
@@ -37,7 +37,9 @@ public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Shuffle/ChannelShuffle.cs b/src/TorchSharp/NN/Shuffle/ChannelShuffle.cs
index f820cae57..65b8e4fdd 100644
--- a/src/TorchSharp/NN/Shuffle/ChannelShuffle.cs
+++ b/src/TorchSharp/NN/Shuffle/ChannelShuffle.cs
@@ -32,7 +32,9 @@ public override string GetName()
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
+
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+
             protected internal override nn.Module _to(ScalarType dtype) => this;
         }
     }
diff --git a/src/TorchSharp/NN/Unflatten.cs b/src/TorchSharp/NN/Unflatten.cs
index 71c7b6a23..fa5954f6b 100644
--- a/src/TorchSharp/NN/Unflatten.cs
+++ b/src/TorchSharp/NN/Unflatten.cs
@@ -12,17 +12,17 @@ namespace Modules
         /// <summary>
         /// This class is used to represent an unflattening operation.
         /// </summary>
-        public sealed class Unflatten : torch.nn.Module<Tensor, Tensor>
+        public sealed class Unflatten : ParamLessModule<Tensor, Tensor>
         {
-            internal Unflatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)
+            internal Unflatten(long dim, long[] unflattenedSize) : base(nameof(Unflatten))
             {
+                this._dim = dim;
+                this._unflattenedSize = unflattenedSize;
             }
 
             public override Tensor forward(Tensor tensor)
             {
-                var res = THSNN_Unflatten_forward(handle, tensor.Handle);
-                if (res == IntPtr.Zero) { torch.CheckForErrors(); }
-                return new Tensor(res);
+                return tensor.unflatten(_dim, _unflattenedSize);
             }
 
             // Rather than spending cycles only to discover that this module has neither
@@ -30,6 +30,9 @@ public override Tensor forward(Tensor tensor)
             protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
             protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
             protected internal override nn.Module _to(ScalarType dtype) => this;
+
+            long _dim;
+            long[] _unflattenedSize;
         }
     }
 
@@ -45,13 +48,7 @@ public static partial class nn
             /// <returns></returns>
             public static Unflatten Unflatten(long dim, long[] unflattenedSize)
             {
-                unsafe {
-                    fixed (long* pUnflattenedSize = unflattenedSize) {
-                        var handle = THSNN_Unflatten_ctor(dim, (IntPtr)pUnflattenedSize, unflattenedSize.Length, out var boxedHandle);
-                        if (handle == IntPtr.Zero) { CheckForErrors(); }
-                        return new Unflatten(handle, boxedHandle);
-                    }
-                }
+                return new Unflatten(dim, unflattenedSize);
             }
         }
     }
diff --git a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs
index efeaec1b3..3fd598823 100644
--- a/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs
+++ b/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs
@@ -1115,96 +1115,6 @@ internal static extern IntPtr THSNN_custom_module(
         [DllImport("LibTorchSharp")]
         internal static extern IntPtr THSNN_LocalResponseNorm_ctor(long size, double alpha, double beta, double k, out IntPtr pBoxedModule);
 
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad1d_ctor(double value, long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad1d_ctor_tuple(double value, long padding_left, long padding_right, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad2d_ctor(double value, long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad2d_ctor_tuple(double value, long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad3d_ctor(double value, long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ConstantPad3d_ctor_tuple(double value, long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad1d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad1d_ctor_tuple(long padding_left, long padding_right, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad2d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad3d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReflectionPad3d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad1d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad1d_ctor_tuple(long padding_left, long padding_right, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad2d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad3d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ReplicationPad3d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ZeroPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ZeroPad2d_ctor(long padding, out IntPtr pBoxedModule);
-
-        [DllImport("LibTorchSharp")]
-        internal static extern IntPtr THSNN_ZeroPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);
-
         [DllImport("LibTorchSharp")]
         internal static extern IntPtr THSNN_AdaptiveAvgPool1d_forward(IntPtr module, IntPtr tensor);
 
diff --git a/test/TorchSharpTest/NN.cs b/test/TorchSharpTest/NN.cs
index 25943888f..455734fc2 100644
--- a/test/TorchSharpTest/NN.cs
+++ b/test/TorchSharpTest/NN.cs
@@ -323,6 +323,8 @@ public void TestIdentity()
                 var input = torch.randn(new long[] { 1, 1000 }, device: device);
                 var output = lin.call(input);
 
+                output[0, 511] = 10; // When we modify the copy, the original should be altered, too.
+
                 Assert.Equal(device.type, output.device_type);
                 Assert.Equal(input.data<float>(), output.data<float>());
             }
@@ -2124,12 +2126,12 @@ public void TestConv1d()
             var shape = new long[] { 16, 3, 28 };
             foreach (var device in TestUtils.AvailableDevices(false)) {
                 Tensor t = torch.rand(shape, device: device);
-                var conv = Conv1d(3, 64, 3, device: device);
+                var conv = Conv1d(3, 64, 5, device: device);
                 var output = conv.call(t);
                 Assert.Equal(device.type, output.device_type);
                 Assert.Equal(16, output.shape[0]);
                 Assert.Equal(64, output.shape[1]);
-                Assert.Equal(26, output.shape[2]);
+                Assert.Equal(24, output.shape[2]);
             }
         }
 
@@ -4700,6 +4702,7 @@ public void TestMultiheadAttention()
             using (var V = torch.tensor(v_data, src_seq_len, batch_size, vembed_dim))
             using (var Attn = torch.tensor(attn_data, batch_size, src_seq_len, src_seq_len)) {
 
+                var children = mha.children().ToList();
                 mha.eval();
                 Assert.False(mha.training);
 
@@ -4892,13 +4895,13 @@ public void TestFlatten()
                     Assert.Equal(new long[] { 32, 360 }, output.shape);
                 }
 
-                using (var flat = Flatten(startDim: 2)) {
+                using (var flat = Flatten(start_dim: 2)) {
                     var output = flat.call(data);
                     Assert.Equal(device.type, output.device_type);
                     Assert.Equal(new long[] { 32, 3, 120 }, output.shape);
                 }
 
-                using (var flat = Flatten(startDim: 0)) {
+                using (var flat = Flatten(start_dim: 0)) {
                     var output = flat.call(data);
                     Assert.Equal(device.type, output.device_type);
                     Assert.Equal(new long[] { 32 * 360 }, output.shape);