code: def test_train_model_distributed_with_sharded_reader(
      self, max_instances_in_memory
  ):
      if torch.cuda.device_count() >= 2:
          devices = [0, 1]
      else:
          devices = [-1, -1]
  
      params = lambda: Params(
          {
              "model": {
                  "type": "simple_tagger",
                  "text_field_embedder": {
                      "token_embedders": {
                          "tokens": {
                              "type": "embedding",
                              "embedding_dim": 5,
                          }
                      }
                  },
                  "encoder": {
                      "type": "lstm",
                      "input_size": 5,
                      "hidden_size": 7,
                      "num_layers": 2,
                  },
              },
              "dataset_reader": {
                  "type": "sharded",
                  "base_reader": {"type": "sequence_tagging"},
              },
              "train_data_path": SEQUENCE_TAGGING_SHARDS_PATH,
              "validation_data_path": SEQUENCE_TAGGING_SHARDS_PATH,
              "data_loader": {
                  "batch_size": 1,
                  "max_instances_in_memory": max_instances_in_memory,
              },
              "trainer": {"num_epochs": 2, "optimizer": "adam"},
              "distributed": {"cuda_devices": devices},
          }
      )
  
      out_dir = os.path.join(self.TEST_DIR, "test_distributed_train")
      train_model(params(), serialization_dir=out_dir)
  
      # Check that some logs specific to distributed
      # training are where we expect.
      serialized_files = os.listdir(out_dir)
      assert "out_worker0.log" in serialized_files
      assert "out_worker1.log" in serialized_files
      assert "model.tar.gz" in serialized_files
  
      # Check we can load the serialized model
      archive = load_archive(out_dir)
      assert archive.model
  
      # Check that we created a vocab from all the shards.
      tokens = archive.model.vocab._token_to_index["tokens"].keys()
      assert tokens == {
          "@@PADDING@@",
          "@@UNKNOWN@@",
          "are",
          ".",
          "animals",
          "plants",
          "vehicles",
          "cats",
          "dogs",
          "snakes",
          "birds",
          "ferns",
          "trees",
          "flowers",
          "vegetables",
          "cars",
          "buses",
          "planes",
          "rockets",
      }
  
      # TODO: This is somewhat brittle. Make these constants in trainer.py.
      train_early = "finishing training early!"
      validation_early = "finishing validation early!"
      train_complete = "completed its entire epoch (training)."
      validation_complete = "completed its entire epoch (validation)."
  
      # There are three shards, but only two workers, so the first worker will have to discard some data.
      with open(os.path.join(out_dir, "out_worker0.log")) as f:
          worker0_log = f.read()
          assert train_early in worker0_log
          assert validation_early in worker0_log
          assert train_complete not in worker0_log
          assert validation_complete not in worker0_log
  
      with open(os.path.join(out_dir, "out_worker1.log")) as f:
          worker1_log = f.read()
          assert train_early not in worker1_log
          assert validation_early not in worker1_log
          assert train_complete in worker1_log
          assert validation_complete in worker1_log
  
imports: import argparse
  import copy
  import json
  import logging
  import math
  import os
  import re
  import shutil
  from collections import OrderedDict, Counter
  from typing import Optional, List, Dict, Any
  import pytest
  import torch
  from allennlp.version import VERSION
  from allennlp.commands.train import Train, train_model, train_model_from_args, TrainModel
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, cpu_or_gpu
  from allennlp.data import Vocabulary
  from allennlp.data.data_loaders import TensorDict
  from allennlp.models import load_archive, Model
  from allennlp.models.archival import CONFIG_NAME
  from allennlp.training import TrainerCallback, GradientDescentTrainer
  from allennlp.training.learning_rate_schedulers import ExponentialLearningRateScheduler, LearningRateScheduler
label_desc: Use image node_id 7 for calling the TestTrain obj's underlying member method code with example usage: obj.test_train_model_distributed_with_sharded_reader(max_instances_in_memory) without return types