code: def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
    
  
    # Save these before they get converted to tensors by the casting below
    params = (scale, bias)
  
    epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
    mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
    variance = tf.reduce_mean(
        tf.squared_difference(x, mean), axis=[-1], keepdims=True)
    norm_x = (x - mean) * tf.rsqrt(variance + epsilon)
  
    output = norm_x * scale + bias
  
  
    return output
docs: Layer norm raw computation.