code: def layer_norm_python(data, gamma, beta, axis, epsilon=1e-5):
      """Layer normalization operator in Python.
  
      Parameters
      ----------
      data : numpy.ndarray
          N-D with shape (d_0, d_1, ..., d_{N-1})
  
      gamma: numpy.ndarray
          K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
  
      beta: numpy.ndarray
          Optional, K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
  
      axis : int or tuple of ints
          Axis over the normalization applied
  
      epsilon : float
          The epsilon value to avoid division by zero.
  
      Returns
      -------
      result : np.ndarray
          N-D with shape (d_0, d_1, ..., d_{N-1})
      """
      mean = np.mean(data, axis, keepdims=True)
      var = np.var(data, axis, keepdims=True)
      result = (data - mean) / np.sqrt(var + epsilon)
      result *= gamma
      if beta is not None:
          result += beta
      return result
  
label_desc: Use image node_id 1 for calling a global function with example usage: layer_norm_python(data, gamma, beta, axis, epsilon) and returns: result