code: def _sigmoid_cross_entropy_with_logits(  # pylint: disable=invalid-name,unused-argument
      _sentinel=None, labels=None, logits=None, name=None
  ):
      if _sentinel is not None:
          raise ValueError(
              "Pass in `label` and `logits` parameters as kwargs"
          )
      return (
          np.maximum(logits, 0)
          - logits * labels
          + np.log1p(np.exp(-np.abs(logits)))
      )
  
file_path: probability/tensorflow_probability/python/internal/backend/numpy/nn.py
imports: import collections
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import _utils
  from tensorflow_probability.python.internal.backend.numpy import numpy_array
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import l2_normalize
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import log_softmax
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import reduce_logsumexp
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import reduce_mean
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import softmax
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import softplus
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import squared_difference
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import top_k
  from tensorflow_probability.python.internal.backend.numpy.ops import stop_gradient
label_desc: Use image node_id 3 for calling a global function with example usage: _sigmoid_cross_entropy_with_logits(_sentinel, labels, logits, name) and returns: unknown