code: def test_without_logits(y_pred_dtype, y_true_dtype, from_logits):
      y_pred = np.asarray(
          [[0.97], [0.91], [0.73], [0.27], [0.09], [0.03]]
      ).astype(y_pred_dtype)
      y_true = np.asarray([[1], [1], [1], [0], [0], [0]]).astype(
          y_true_dtype
      )
      if from_logits:
          y_pred = np.log(y_pred / (1.0 - y_pred))
  
      # When alpha and gamma are None, the result is equal to BCE.
      fl = focal_loss.sigmoid_focal_crossentropy(
          y_true=y_true,
          y_pred=y_pred,
          alpha=None,
          gamma=None,
          from_logits=from_logits,
      ).numpy()
      bce = tf.keras.losses.binary_crossentropy(
          y_true, y_pred, from_logits=from_logits
      ).numpy()
      np.testing.assert_allclose(fl, bce)
  
      # When gamma is 2.0.
      fl = focal_loss.sigmoid_focal_crossentropy(
          y_true=y_true,
          y_pred=y_pred,
          alpha=None,
          gamma=2.0,
          from_logits=from_logits,
      ).numpy()
      order_of_ratio = np.power(10.0, np.floor(np.log10(bce / fl)))
      pow_values = np.asarray([1000, 100, 10, 10, 100, 1000])
      np.testing.assert_allclose(order_of_ratio, pow_values)
  
imports: import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.losses import focal_loss
label_desc: Use image node_id 3 for calling a global function with example usage: test_without_logits(y_pred_dtype, y_true_dtype, from_logits) without return types