class_docstr: Salience map from gradient L2 norm.
class_docstr_tok: ["Salience","map","from","gradient","L2","norm","."]
code: def _interpret(self, grads: np.ndarray, tokens: np.ndarray):
      assert grads.shape[0] == len(tokens)
      # Norm of dy/d(embs)
      grad_norm = np.linalg.norm(grads, axis=1)
      grad_norm /= np.sum(grad_norm)
      # <float32>[num_tokens]
      return grad_norm
  
imports: from typing import cast, Optional
  from absl import logging
  from lit_nlp.api import components
  from lit_nlp.api import dataset
  from lit_nlp.api import dtypes
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.components.citrus import utils
  from lit_nlp.lib import utils
  import numpy
label_desc: Use image node_id 2 for calling the GradientNorm obj's underlying member method code with example usage: obj._interpret(grads, tokens) and returns: grad_norm