code: def l2_normalize(incoming, dim, epsilon=1e-12, name="l2_normalize"):
      """L2 Normalization.
  
      Normalizes along dimension `dim` using an L2 norm.
  
      For a 1-D tensor with `dim = 0`, computes
      ```
      output = x / sqrt(max(sum(x**2), epsilon))
      ```
  
      For `x` with more dimensions, independently normalizes each 1-D slice along
      dimension `dim`.
  
      Arguments:
          incoming: `Tensor`. Incoming Tensor.
          dim: `int`. Dimension along which to normalize.
          epsilon: `float`. A lower bound value for the norm. Will use
              `sqrt(epsilon)` as the divisor if `norm < sqrt(epsilon)`.
          name: `str`. A name for this layer (optional).
  
      Returns:
        A `Tensor` with the same shape as `x`.
      """
      with tf.name_scope(name) as name:
          x = tf.convert_to_tensor(incoming, name="x")
          square_sum = tf.reduce_sum(
              tf.square(x), [dim], keep_dims=True
          )
          x_inv_norm = tf.rsqrt(tf.maximum(square_sum, epsilon))
  
      return tf.multiply(x, x_inv_norm, name=name)
  
file_path: tflearn/tflearn/layers/normalization.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from tensorflow.python.training import moving_averages
  import tflearn
  from ..None import utils
  from ..None import variables
  from ..utils import get_from_module
label_desc: Use image node_id 4 for calling a global function with example usage: l2_normalize(incoming, dim, epsilon, name) and returns: tf