code: def test_model_size_precision(tmpdir, accelerator):
      """Test model size for half and full precision."""
      model = PreCalculatedModel()
  
      # fit model
      trainer = Trainer(
          default_root_dir=tmpdir,
          accelerator=accelerator,
          devices=1,
          max_steps=1,
          max_epochs=1,
          precision=32,
      )
      trainer.fit(model)
      summary = summarize(model)
      assert model.pre_calculated_model_size == summary.model_size
  
imports: from collections import OrderedDict
  from typing import Any
  import pytest
  import torch
  import torch.nn
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import LightningModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.utilities.model_summary.model_summary import LEFTOVER_PARAMS_NAME, NOT_APPLICABLE, UNKNOWN_SIZE, ModelSummary, summarize
  from tests_pytorch.helpers.advanced_models import ParityModuleRNN
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 13 for calling a global function with example usage: test_model_size_precision(tmpdir, accelerator) without return types