# Turning quantum nodes into Torch Layers
**URL:** https://discuss.pennylane.ai/t/turning-quantum-nodes-into-torch-layers/3395
**Category:** PennyLane Help
**Created:** [September 5, 2023, 4:11pm UTC](https://discuss.pennylane.ai/t/turning-quantum-nodes-into-torch-layers/3395 "2023-09-05T16:11:33Z")
**Posts on this page:** 3
**Page:** 2
### Author:  [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)
#### Post date: [January 10, 2024, 8:38pm UTC](https://discuss.pennylane.ai/t/turning-quantum-nodes-into-torch-layers/3395/21 "2024-01-10T20:38:11Z")
Hey @kevinkawchak,
You can use the `compute_decomposition` method that each operator has:
```pythyon
inputs = np.random.uniform(0, np.pi, size=(n_qubits,))
print(qml.AngleEmbedding.compute_decomposition(inputs, wires=range(n_qubits), rotation=qml.RY))
n_layers = 10
weights = np.random.uniform(0, np.pi, size=(n_layers, n_qubits))
print(qml.RandomLayers.compute_decomposition(weights, wires=range(n_qubits), ratio_imprim=0, imprimitive=qml.CNOT, rotations=[qml.RY], seed=42))
```
```auto
[RY(tensor(1.42790738, requires_grad=True), wires=[0]), RY(tensor(2.82650401, requires_grad=True), wires=[1]), RY(tensor(1.51604305, requires_grad=True), wires=[2]), RY(tensor(2.66255654, requires_grad=True), wires=[3])]
[RY(tensor(1.1522639, requires_grad=True), wires=[2]), RY(tensor(2.30919675, requires_grad=True), wires=[1]), RY(tensor(2.99291522, requires_grad=True), wires=[0]), RY(tensor(1.87356346, requires_grad=True), wires=[0]), RY(tensor(1.49890151, requires_grad=True), wires=[2]), RY(tensor(0.42741971, requires_grad=True), wires=[3]), RY(tensor(1.76770254, requires_grad=True), wires=[2]), RY(tensor(3.06895537, requires_grad=True), wires=[1]), RY(tensor(0.6608513, requires_grad=True), wires=[1]), RY(tensor(3.00064304, requires_grad=True), wires=[3]), RY(tensor(2.6624893, requires_grad=True), wires=[0]), RY(tensor(2.98035507, requires_grad=True), wires=[2]), RY(tensor(2.35717625, requires_grad=True), wires=[1]), RY(tensor(1.89289895, requires_grad=True), wires=[2]), RY(tensor(1.71322966, requires_grad=True), wires=[0]), RY(tensor(2.89751162, requires_grad=True), wires=[3]), RY(tensor(0.75892838, requires_grad=True), wires=[3]), RY(tensor(1.13771258, requires_grad=True), wires=[0]), RY(tensor(1.7231123, requires_grad=True), wires=[2]), RY(tensor(2.94940615, requires_grad=True), wires=[0]), RY(tensor(0.252351, requires_grad=True), wires=[1]), RY(tensor(0.16210348, requires_grad=True), wires=[3]), RY(tensor(0.78967891, requires_grad=True), wires=[0]), RY(tensor(0.86227181, requires_grad=True), wires=[1]), RY(tensor(2.77279583, requires_grad=True), wires=[2]), RY(tensor(0.64170049, requires_grad=True), wires=[1]), RY(tensor(0.4653104, requires_grad=True), wires=[3]), RY(tensor(0.73046264, requires_grad=True), wires=[1]), RY(tensor(2.32315717, requires_grad=True), wires=[0]), RY(tensor(0.86651552, requires_grad=True), wires=[1]), RY(tensor(1.60561273, requires_grad=True), wires=[3]), RY(tensor(2.40816571, requires_grad=True), wires=[1]), RY(tensor(1.53504934, requires_grad=True), wires=[2]), RY(tensor(2.62760047, requires_grad=True), wires=[0]), RY(tensor(2.28099916, requires_grad=True), wires=[3]), RY(tensor(2.67712636, requires_grad=True), wires=[3]), RY(tensor(2.15453193, requires_grad=True), wires=[1]), RY(tensor(1.13060742, requires_grad=True), wires=[3]), RY(tensor(1.84518167, requires_grad=True), wires=[0]), RY(tensor(2.49053654, requires_grad=True), wires=[0])]
```
From there you can just count the number of `RY` operators 🙂. There are more elegant ways of doing this, but this is short and to the point 👍
---
### Author:  [@kevinkawchak](https://discuss.pennylane.ai/u/kevinkawchak)
#### Post date: [February 6, 2024, 8:57am UTC](https://discuss.pennylane.ai/t/turning-quantum-nodes-into-torch-layers/3395/22 "2024-02-06T08:57:22Z")
Hello, how can I insert torch layers into this demo?:
'[Transformers-Tutorials/VisionTransformer/Fine\_tuning\_the\_Vision\_Transformer\_on\_CIFAR\_10\_with\_PyTorch\_Lightning.ipynb at master · NielsRogge/Transformers-Tutorials · GitHub](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb)
```auto
class ViTLightningModule(pl.LightningModule):
def __init__ (self, num_labels=10):
super(ViTLightningModule, self). __init__ ()
self.vit = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k',
num_labels=10,
id2label=id2label,
label2id=label2id)
self.qlayer_1 = qml.qnn.TorchLayer(qnode, weight_shapes)
def forward(self, pixel_values):
outputs = self.vit(pixel_values=pixel_values)
outputs = self.qlayer_1(outputs)
return outputs.logits
```
```auto
Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False
INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores
INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs
INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs
INFO:pytorch_lightning.callbacks.model_summary:
| Name | Type | Params
-------------------------------------------------------
0 | vit | ViTForImageClassification | 85.8 M
1 | qlayer_1 | TorchLayer | 6
-------------------------------------------------------
85.8 M Trainable params
0 Non-trainable params
85.8 M Total params
343.225 Total estimated model params size (MB)
Sanity Checking DataLoader 0: 0%
0/2 [00:00 in ()
13 model = ViTLightningModule()
14 trainer = Trainer(callbacks=[EarlyStopping(monitor='validation_loss')])
---> 15 trainer.fit(model)
18 frames
/usr/local/lib/python3.10/dist-packages/pennylane/qnn/torch.py in forward(self, inputs)
392 tensor: output data
393 """
--> 394 has_batch_dim = len(inputs.shape) > 1
395
396 # in case the input has more than one batch dimension
AttributeError: 'ImageClassifierOutput' object has no attribute 'shape'
```
---
### Author:  [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)
#### Post date: [February 6, 2024, 10:51pm UTC](https://discuss.pennylane.ai/t/turning-quantum-nodes-into-torch-layers/3395/23 "2024-02-06T22:51:27Z")
Hey @kevinkawchak,
So long as you’re not mixing Torch’s GPU and PL-LightningGPU then it should be fine. This works:
```auto
import pennylane as qml
from pennylane import numpy as np
import torch
dev = qml.device("default.qubit", wires=2)
@qml.qnode(dev)
def circuit(inputs, weights):
qml.AmplitudeEmbedding(inputs, wires=range(2), normalize=True)
qml.RX(weights, wires=0)
return qml.expval(qml.PauliZ(0))
weight_shapes = {"weights": 1}
torch_layer = qml.qnn.TorchLayer(circuit, weight_shapes)
clayer = torch.nn.Linear(2, 4)
model = torch.nn.Sequential(clayer, torch_layer)
x = np.random.uniform(0, 1, size=(2,))
x = torch.tensor(x).float()
model(x)
# tensor(0.3270, grad_fn=)
```
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