import torch
import torch.optim as optim
import torch.nn.functional as F

class EnsembleOptimizer:
def __init__(self, models, optimizers):
self.models = models
self.optimizers = optimizers

def zero_grad(self):
for optimizer in self.optimizers:
optimizer.zero_grad()

def step(self):
for optimizer in self.optimizers:
optimizer.step()

def model_averaging(self, inputs):
outputs = []
for model in self.models:
output = model(inputs)
outputs.append(output)
return torch.mean(torch.stack(outputs), dim=0)

def model_stacking(self, inputs):
outputs = []
for model in self.models:
output = model(inputs)
outputs.append(output)
return torch.cat(outputs, dim=1)

def model_boosting(self, inputs):
outputs = []
for model, optimizer in zip(self.models, self.optimizers):
optimizer.zero_grad()
output = model(inputs)
output.backward()
optimizer.step()
outputs.append(output)
return torch.sum(torch.stack(outputs), dim=0)

# Example usage

# Create your models
model1 = YourModel1()
model2 = YourModel2()
model3 = YourModel3()

# Create the optimizers for each model
optimizer1 = optim.Adam(model1.parameters(), lr=0.001)
optimizer2 = optim.SGD(model2.parameters(), lr=0.01)
optimizer3 = optim.RMSprop(model3.parameters(), lr=0.001)

# Create the ensemble optimizer
ensemble_optimizer = EnsembleOptimizer(models=[model1, model2, model3], optimizers=[optimizer1, optimizer2, optimizer3])

# Example training loop
for epoch in range(num_epochs):
for batch_data in dataloader:
inputs, targets = batch_data

# Zero the gradients
ensemble_optimizer.zero_grad()

# Perform ensemble optimization (choose one of the methods)
outputs = ensemble_optimizer.model_averaging(inputs)
# outputs = ensemble_optimizer.model_stacking(inputs)
# outputs = ensemble_optimizer.model_boosting(inputs)

# Compute the loss
loss = compute_loss(outputs, targets)

# Backward pass
loss.backward()

# Update the parameters
ensemble_optimizer.step()