code: def rms_norm_python(data, weight, axis, epsilon=1e-5):
      """Root mean square normalization operator in Python.
  
      Parameters
      ----------
      data : numpy.ndarray
          N-D with shape (d_0, d_1, ..., d_{N-1})
  
      weight: numpy.ndarray
          K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
  
      bias: 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})
      """
      dtype = data.dtype
      data = data.astype("float32")
      weight = weight.astype("float32")
      square_mean = np.mean(np.square(data), axis, keepdims=True)
      result = data * weight / np.sqrt(square_mean + epsilon)
      return result.astype(dtype)
  
label_desc: Use image node_id 1 for calling a global function with example usage: rms_norm_python(data, weight, axis, epsilon) and returns: result