How to use pycma for parameter optimization problem of neural network,eg, weights and biase in Pytorch

[Learn2Learn]

Here is a simple code of fully-connected neural network in Pytorch.
then ,how to optimize model.parameters() ,which includes weights and biases of neural net work.
I am trying to solve the problem with your pycma but have no idea how to implement it in Pytorch.Could you please give some advises？thank you in advance!

`import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

Hyper-parameters

input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001

Toy dataset

x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
[9.779], [6.182], [7.59], [2.167], [7.042],
[10.791], [5.313], [7.997], [3.1]], dtype=np.float32)

y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
[3.366], [2.596], [2.53], [1.221], [2.827],
[3.465], [1.65], [2.904], [1.3]], dtype=np.float32)

Linear regression model

model = nn.Linear(input_size, output_size)

Loss and optimizer

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

Train the model

for epoch in range(num_epochs):
# Convert numpy arrays to torch tensors
inputs = torch.from_numpy(x_train)
targets = torch.from_numpy(y_train)

# Forward pass
outputs = model(inputs)
loss = criterion(outputs, targets)

# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()

if (epoch+1) % 5 == 0:
    print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

Plot the graph

predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()

Save the model checkpoint

torch.save(model.state_dict(), 'model.ckpt')`