code: def gelu(x, approximate=False):
      """Applies the Gaussian error linear unit (GELU) activation function.
  
      Gaussian error linear unit (GELU) computes
      `x * P(X <= x)`, where `P(X) ~ N(0, 1)`.
      The (GELU) nonlinearity weights inputs by their value, rather than gates
      inputs by their sign as in ReLU.
  
      For example:
  
      >>> x = tf.constant([-3.0, -1.0, 0.0, 1.0, 3.0], dtype=tf.float32)
      >>> y = tf.keras.activations.gelu(x)
      >>> y.numpy()
      array([-0.00404951, -0.15865529,  0.        ,  0.8413447 ,  2.9959507 ],
          dtype=float32)
      >>> y = tf.keras.activations.gelu(x, approximate=True)
      >>> y.numpy()
      array([-0.00363752, -0.15880796,  0.        ,  0.841192  ,  2.9963627 ],
          dtype=float32)
  
      Args:
          x: Input tensor.
          approximate: A `bool`, whether to enable approximation.
  
      Returns:
          The gaussian error linear activation:
          `0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))`
          if `approximate` is `True` or
          `x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`,
          where `P(X) ~ N(0, 1)`,
          if `approximate` is `False`.
  
      Reference:
        - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415)
      """
      return nn.gelu(x, approximate)
  
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 8 for calling a global function with example usage: gelu(x, approximate) and returns: nn