code: def test_train_and_evaluate(self):
      from allennlp.commands.train import train_model
      from allennlp.commands.evaluate import evaluate_from_args
      import argparse
      from allennlp.commands import Evaluate
  
      model_name = "epwalsh/bert-xsmall-dummy"
  
      def reader():
          return {
              "type": "text_classification_json",
              "tokenizer": {
                  "type": "pretrained_transformer",
                  "model_name": model_name,
              },
              "token_indexers": {
                  "tokens": {
                      "type": "pretrained_transformer",
                      "model_name": model_name,
                  }
              },
          }
  
      def head():
          return {
              "type": "classifier",
              "seq2vec_encoder": {
                  "type": "cls_pooler",
                  "embedding_dim": 20,
              },
              "input_dim": 20,
              "num_labels": 2,
          }
  
      head_eins_input = "test_fixtures/data/text_classification_json/imdb_corpus.jsonl"
      head_zwei_input = "test_fixtures/data/text_classification_json/ag_news_corpus_fake_sentiment_labels.jsonl"
  
      params = Params(
          {
              "dataset_reader": {
                  "type": "multitask",
                  "readers": {
                      "head_eins": reader(),
                      "head_zwei": reader(),
                  },
              },
              "model": {
                  "type": "multitask",
                  "backbone": {
                      "type": "pretrained_transformer",
                      "model_name": model_name,
                  },
                  "heads": {
                      "head_eins": head(),
                      "head_zwei": head(),
                  },
                  "arg_name_mapping": {"backbone": {"tokens": "text"}},
              },
              "train_data_path": {
                  "head_eins": head_eins_input,
                  "head_zwei": head_zwei_input,
              },
              "data_loader": {
                  "type": "multitask",
                  "scheduler": {"batch_size": 2},
              },
              "trainer": {
                  "optimizer": {
                      "type": "huggingface_adamw",
                      "lr": 4e-5,
                  },
                  "num_epochs": 2,
              },
          }
      )
  
      serialization_dir = os.path.join(
          self.TEST_DIR, "serialization_dir"
      )
      train_model(params, serialization_dir=serialization_dir)
  
      args = [
          "evaluate",
          str(self.TEST_DIR / "serialization_dir"),
          f'{{"head_eins": "{head_eins_input}", "head_zwei": "{head_zwei_input}"}}',
          "--output-file",
          str(self.TEST_DIR / "output.txt"),
          "--predictions-output-file",
          str(self.TEST_DIR / "predictions.json"),
      ]
  
      parser = argparse.ArgumentParser(description="Testing")
      subparsers = parser.add_subparsers(title="Commands", metavar="")
      Evaluate().add_subparser(subparsers)
      args = parser.parse_args(args)
      metrics = evaluate_from_args(args)
      assert "head_eins_accuracy" in metrics
      assert "head_zwei_accuracy" in metrics
  
file_path: allennlp/tests/models/multitask_test.py
imports: import os
  import pytest
  from allennlp.common import Params
  from allennlp.common.testing import ModelTestCase
  from allennlp.data import Instance, Vocabulary, Batch
  from allennlp.data.fields import LabelField, TextField, MetadataField
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.models.heads import ClassifierHead
  from allennlp.models import MultiTaskModel
  from allennlp.modules.backbones import PretrainedTransformerBackbone
  from allennlp.modules.seq2vec_encoders import ClsPooler
label_desc: Use image node_id 2 for calling the TestMultiTaskModel obj's underlying member method code with example usage: obj.test_train_and_evaluate() and returns: dict, dict