docstr: Creates the DataLoader from the configuration object
  
  Args:
      cfg: configuration object
  
  Returns:
      DataLoader for model's data
docstr_tok: ["Creates","the","DataLoader","from","the","configuration","object","Args",":","cfg",":","configuration","object","Returns",":","DataLoader","for","model","'s","data"]
code: def _setup_dataloader_from_config(
      self, cfg: DictConfig
  ) -> DataLoader:
      """
      Creates the DataLoader from the configuration object
  
      Args:
          cfg: configuration object
  
      Returns:
          DataLoader for model's data
      """
  
      input_file = f"{self.data_dir}/{cfg.prefix}.tsv"
      slot_file = f"{self.data_dir}/{cfg.prefix}_slots.tsv"
      intent_dict_file = self.data_dir + "/dict.intents.csv"
  
      lines = open(intent_dict_file, "r").readlines()
      lines = [line.strip() for line in lines if line.strip()]
      num_intents = len(lines)
  
      if not (os.path.exists(input_file) and os.path.exists(slot_file)):
          raise FileNotFoundError(
              f"{input_file} or {slot_file} not found. Please refer to the documentation for the right format \
                   of Intents and Slots files."
          )
  
      dataset = MultiLabelIntentSlotClassificationDataset(
          input_file=input_file,
          slot_file=slot_file,
          num_intents=num_intents,
          tokenizer=self.tokenizer,
          max_seq_length=self.max_seq_length,
          num_samples=cfg.num_samples,
          pad_label=self.cfg.data_desc.pad_label,
          ignore_extra_tokens=self.cfg.ignore_extra_tokens,
          ignore_start_end=self.cfg.ignore_start_end,
      )
  
      return DataLoader(
          dataset=dataset,
          batch_size=cfg.batch_size,
          shuffle=cfg.shuffle,
          num_workers=cfg.num_workers,
          pin_memory=cfg.pin_memory,
          drop_last=cfg.drop_last,
          collate_fn=dataset.collate_fn,
      )
  
imports: import os
  from typing import List, Optional, Tuple
  import numpy
  import numpy.typing
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from sklearn.metrics import f1_score, precision_score, recall_score
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, BCEWithLogitsLoss, CrossEntropyLoss
  from nemo.collections.nlp.data.intent_slot_classification import MultiLabelIntentSlotClassificationDataset, MultiLabelIntentSlotDataDesc
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport, MultiLabelClassificationReport
  from nemo.collections.nlp.models.intent_slot_classification import IntentSlotClassificationModel
  from nemo.collections.nlp.modules.common import SequenceTokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the MultiLabelIntentSlotClassificationModel obj's underlying member method code with example usage: obj._setup_dataloader_from_config(cfg) and returns: DataLoader