code: def test_randomly_connected_dense_output_always_dense(
      inputs: np.array, units: int, expected_num_non_zero_outputs: int
  ):
      layer = RandomlyConnectedDense(
          density=0.0, units=units, use_bias=False
      )
      y = layer(inputs)
      num_non_zero_outputs = tf.math.count_nonzero(y).numpy()
      assert num_non_zero_outputs == expected_num_non_zero_outputs
  
imports: from typing import Text, List, Tuple, Union, Optional
  import pytest
  from _pytest.monkeypatch import MonkeyPatch
  import numpy
  import tensorflow
  from rasa.utils.tensorflow.layers import DotProductLoss, MultiLabelDotProductLoss, RandomlyConnectedDense, DenseForSparse
  from rasa.utils.tensorflow.constants import INNER, SOFTMAX, LABEL, LABEL_PAD_ID
  import rasa.utils.tensorflow.layers_utils
  from rasa.shared.nlu.constants import TEXT, INTENT, ACTION_NAME, ACTION_TEXT, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
  from rasa.core.constants import DIALOGUE
label_desc: Use image node_id 11 for calling a global function with example usage: test_randomly_connected_dense_output_always_dense(inputs, units, expected_num_non_zero_outputs) without return types