code: def test_distilbert_param_updates(trainable: bool):
      max_sequence_length = 20
      distil_bert_encoder = text_encoders.DistilBERTEncoder(
          use_pretrained=False,
          max_sequence_length=max_sequence_length,
          trainable=trainable,
      )
  
      # send a random input through the model with its initial weights
      inputs = torch.rand((2, max_sequence_length)).type(
          distil_bert_encoder.input_dtype
      )
      outputs = distil_bert_encoder(inputs)
  
      # perform a backward pass to update the model params
      target = torch.randn(outputs[ENCODER_OUTPUT].shape)
      check_module_parameters_updated(
          distil_bert_encoder, (inputs,), target
      )
  
      # send the same input through the model again. should be different if trainable, else the same
      outputs2 = distil_bert_encoder(inputs)
  
      encoder_output1 = outputs[ENCODER_OUTPUT]
      encoder_output2 = outputs2[ENCODER_OUTPUT]
  
      if trainable:
          # Outputs should be different if the model was updated
          assert not torch.equal(encoder_output1, encoder_output2)
      else:
          # Outputs should be the same if the model wasn't updated
          assert torch.equal(encoder_output1, encoder_output2)
  
imports: import json
  import os
  from typing import Optional, Type, Union
  from unittest import mock
  import pytest
  import torch
  import ludwig.schema.encoders.utils
  from ludwig.api import LudwigModel
  from ludwig.constants import ENCODER, ENCODER_OUTPUT, MODEL_ECD, NAME, TEXT, TRAINER
  from ludwig.encoders import text_encoders
  from ludwig.error import ConfigValidationError
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils.data_utils import load_json
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
  from tests.integration_tests.utils import category_feature, clear_huggingface_cache, generate_data, HF_ENCODERS, LocalTestBackend, text_feature
label_desc: Use image node_id 6 for calling a global function with example usage: test_distilbert_param_updates(trainable) without return types