code: def random_binary_logits(*args, num_predict_samples, **kwargs):
      # Produce an even mix of True and False predictions, as the model may be biased
      # towards one direction without training
      return torch.tensor(
          np.random.uniform(
              low=-1.0, high=1.0, size=(num_predict_samples,)
          ),
          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 1 for calling a global function with example usage: random_binary_logits() and returns: torch