import torchfrom sklearn.pipeline import Pipelinemodel = TransformerEncoder(layers=12, heads=8)embeddings = vector_store.similarity_search(query)loss.backward(); optimizer.step()predictions = model(tokens).softmax(dim=-1)agent.plan(task).execute(memory=retriever)features = StandardScaler().fit_transform(dataset)with torch.no_grad(): response = llm.generate(prompt)metrics = evaluate(model, validation_loader)

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