code: def _generate_doc_classification_inputs(bsize: int, num_labels: int):
      return torch.randn(bsize, num_labels)
  
imports: import random
  import string
  import sys
  from contextlib import contextmanager
  from io import StringIO
  from typing import Dict, List
  import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  import numpy
  import torch
  from hypothesis import given, settings
  from pytext.common.constants import SpecialTokens
  from pytext.data.tensorizers import LabelTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.loss import CrossEntropyLoss
  from pytext.models.output_layers.doc_classification_output_layer import ClassificationOutputLayer
  from pytext.models.output_layers.intent_slot_output_layer import IntentSlotOutputLayer
  from pytext.models.output_layers.word_tagging_output_layer import CRFOutputLayer, WordTaggingOutputLayer
label_desc: Use image node_id 8 for calling the OutputLayerTest obj's underlying member method code with example usage: obj._generate_doc_classification_inputs(bsize, num_labels) and returns: torch