code: def random_set_logits(
      *args, num_predict_samples, vocab_size, pct_positive, **kwargs
  ):
      # Produce a desired mix of predictions based on the pct_positive, as the model may be biased
      # towards one direction without training
      num_positive = int(num_predict_samples * pct_positive)
      num_negative = num_predict_samples - num_positive
      negative_logits = np.random.uniform(
          low=-1.0, high=-0.1, size=(num_negative, vocab_size)
      )
      positive_logits = np.random.uniform(
          low=0.1, high=1.0, size=(num_positive, vocab_size)
      )
      logits = np.concatenate(
          [negative_logits, positive_logits], axis=0
      )
      return torch.tensor(logits, dtype=torch.float32)
  
file_path: ludwig/tests/integration_tests/test_postprocessing.py
imports: import os
  from functools import partial
  from unittest import mock
  import numpy
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.constants import BATCH_SIZE, DECODER, NAME, TRAINER
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, RAY_BACKEND_CONFIG, set_feature, text_feature
label_desc: Use image node_id 2 for calling a global function with example usage: random_set_logits() and returns: torch