code: def _get_dataloader(
      dataset_type,
      dataset_dir,
      num_speakers,
      sample_rate,
      batch_size,
      task=None,
  ):
      (
          train_dataset,
          valid_dataset,
          eval_dataset,
      ) = dataset_utils.get_dataset(
          dataset_type, dataset_dir, num_speakers, sample_rate, task
      )
      train_collate_fn = dataset_utils.get_collate_fn(
          dataset_type,
          mode="train",
          sample_rate=sample_rate,
          duration=4,
      )
  
      test_collate_fn = dataset_utils.get_collate_fn(
          dataset_type, mode="test"
      )
  
      train_loader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=batch_size,
          sampler=torch.utils.data.distributed.DistributedSampler(
              train_dataset
          ),
          collate_fn=train_collate_fn,
          pin_memory=True,
      )
      valid_loader = torch.utils.data.DataLoader(
          valid_dataset,
          batch_size=batch_size,
          sampler=torch.utils.data.distributed.DistributedSampler(
              valid_dataset
          ),
          collate_fn=test_collate_fn,
          pin_memory=True,
      )
      eval_loader = torch.utils.data.DataLoader(
          eval_dataset,
          batch_size=batch_size,
          sampler=torch.utils.data.distributed.DistributedSampler(
              eval_dataset
          ),
          collate_fn=test_collate_fn,
          pin_memory=True,
      )
      return train_loader, valid_loader, eval_loader
  
file_path: audio/examples/source_separation/conv_tasnet/train.py
imports: import argparse
  import pathlib
  import time
  import conv_tasnet
  import torch
  import torchaudio
  import torchaudio.models
  from utils import dist_utils
  from utils.dataset import utils
label_desc: Use image node_id 3 for calling a global function with example usage: _get_dataloader(dataset_type, dataset_dir, num_speakers, sample_rate, batch_size, task) and returns: train_loader,  valid_loader,  eval_loader