docstr: Get prediction probabilities for the queries (intent and slots)
  
  Args:
      queries: text sequences
      test_ds: Dataset configuration section.
  
  Returns:
      numpy array of intent probabilities
docstr_tok: ["Get","prediction","probabilities","for","the","queries","(","intent","and","slots",")","Args",":","queries",":","text","sequences","test_ds",":","Dataset","configuration","section",".","Returns",":","numpy","array","of","intent","probabilities"]
code: def prediction_probabilities(
      self, queries: List[str], test_ds: DictConfig
  ) -> npt.NDArray:
      """
      Get prediction probabilities for the queries (intent and slots)
  
      Args:
          queries: text sequences
          test_ds: Dataset configuration section.
  
      Returns:
          numpy array of intent probabilities
      """
  
      probabilities = []
  
      mode = self.training
      try:
          device = "cuda" if torch.cuda.is_available() else "cpu"
  
          # Switch model to evaluation mode
          self.eval()
          self.to(device)
  
          # Dataset.
          infer_datalayer = self._setup_infer_dataloader(
              queries, test_ds
          )
  
          for batch in infer_datalayer:
              (
                  input_ids,
                  input_type_ids,
                  input_mask,
                  loss_mask,
                  subtokens_mask,
              ) = batch
  
              intent_logits, slot_logits = self.forward(
                  input_ids=input_ids.to(device),
                  token_type_ids=input_type_ids.to(device),
                  attention_mask=input_mask.to(device),
              )
  
              # predict intents for these examples
              probabilities.append(
                  torch.sigmoid(intent_logits).detach().cpu().numpy()
              )
  
          probabilities = np.concatenate(probabilities)
  
      finally:
          # set mode back to its original value
          self.train(mode=mode)
  
      return probabilities
  
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 6 for calling the MultiLabelIntentSlotClassificationModel obj's underlying member method code with example usage: obj.prediction_probabilities(queries, test_ds) and returns: probabilities