import torch import torch.nn as nn import torch.optim as optim from torch.nn.parallel import DataParallel # Define a model architecture class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.conv = nn.Conv2d(3, 64, kernel_size=3, padding=1) self.relu = nn.ReLU() def forward(self, x): x = self.conv(x) x = self.relu(x) return x # Instantiate the model and wrap it in DataParallel module model = MyModel() model = DataParallel(model) # Define loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # Prepare input data input_data = torch.randn(32, 3, 224, 224) # Forward pass, compute loss and gradients, perform backpropagation output = model(input_data) target = torch.randint(0, 10, (32,)) loss = criterion(output, target) loss.backward() optimizer.step()In this example, a simple convolutional neural network model is defined and wrapped in the DataParallel module. The loss function and optimizer are defined as usual, and the input data is generated randomly. The forward pass, loss computation, and backpropagation are performed using the model object, which is now a parallelized version of the original model.