class_docstr: The parameters and inputs of this model have different dtypes.
class_docstr_tok: ["The","parameters","and","inputs","of","this","model","have","different","dtypes","."]
code: def __init__(self):
      super().__init__()
      self.embed = nn.Embedding(10, 20)  # expects dtype long as input
      self.reduce = nn.Linear(20, 1)  # dtype: float
      self.example_input_array = torch.tensor([[0, 2, 1], [3, 5, 3]])
  
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 1 to create a new MixedDtypeModel object from inherited base classes: LightningModule with example: obj = MixedDtypeModel()