code: def test_mixed_dtype_model_summary():
      """Test that the model summary works with models that have mixed input- and parameter dtypes."""
      model = MixedDtypeModel()
      summary = summarize(model)
      assert summary.in_sizes == [[2, 3], [2, 3, 20]]  # embed  # reduce
      assert summary.out_sizes == [[2, 3, 20], [2, 3, 1]]
  
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 4 for calling a global function with example usage: test_mixed_dtype_model_summary() without return types