code: def sigmoid(x):
      """Sigmoid activation function.
  
      It is defined as: `sigmoid(x) = 1 / (1 + exp(-x))`.
  
      For small values (<-5),
      `sigmoid` returns a value close to zero, and for large values (>5)
      the result of the function gets close to 1.
  
      Sigmoid is equivalent to a 2-element softmax, where the second element is
      assumed to be zero. The sigmoid function always returns a value between
      0 and 1.
  
      Args:
          x: Input tensor.
      """
      output = ops.sigmoid(x)
      # Cache the logits to use for crossentropy loss.
      try:
          output._keras_logits = x
      except AttributeError:
          # We're dealing with a C-type.
          pass
      return output
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 12 for calling a global function with example usage: sigmoid(x) and returns: output