code: def example_experts():
      expert = Linear(1, 1)
      opt = torch.optim.SGD(expert.parameters(), PEAK_LR)
  
      args_schema = (BatchTensorDescriptor(1),)
      expert_backend = ModuleBackend(
          name=EXPERT_NAME,
          module=expert,
          optimizer=opt,
          scheduler=get_linear_schedule_with_warmup(
              opt,
              num_warmup_steps=BACKWARD_PASSES_BEFORE_SAVE,
              num_training_steps=BACKWARD_PASSES_BEFORE_SAVE
              + BACKWARD_PASSES_AFTER_SAVE,
          ),
          args_schema=args_schema,
          outputs_schema=BatchTensorDescriptor(1),
          max_batch_size=1,
      )
      experts = {EXPERT_NAME: expert_backend}
      yield experts
  
file_path: hivemind/tests/test_expert_backend.py
imports: from pathlib import Path
  from tempfile import TemporaryDirectory
  import pytest
  import torch
  from torch.nn import Linear
  from hivemind import BatchTensorDescriptor, ModuleBackend
  from hivemind.moe.server.checkpoints import load_experts, store_experts
  from hivemind.moe.server.layers.lr_schedule import get_linear_schedule_with_warmup
label_desc: Use image node_id 1 for calling a global function with example usage: example_experts() without return types