code: def sigmoid(x):
      """Sigmoid activation function, `sigmoid(x) = 1 / (1 + exp(-x))`.
  
      Applies the sigmoid activation function. 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.
  
      For example:
  
      >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)
      >>> b = tf.keras.activations.sigmoid(a)
      >>> b.numpy()
      array([2.0611537e-09, 2.6894143e-01, 5.0000000e-01, 7.3105860e-01,
               1.0000000e+00], dtype=float32)
  
      Args:
          x: Input tensor.
  
      Returns:
          Tensor with the sigmoid activation: `1 / (1 + exp(-x))`.
      """
      output = nn.sigmoid(x)
      # Cache the logits to use for crossentropy loss.
      output._keras_logits = x  # pylint: disable=protected-access
      return output
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.layers import advanced_activations
  from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
  from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.util import dispatch
label_desc: Use image node_id 10 for calling a global function with example usage: sigmoid(x) and returns: output