class_docstr: Salience map using the values of gradient * input as attribution.
class_docstr_tok: ["Salience","map","using","the","values","of","gradient","*","input","as","attribution","."]
code: def _interpret(self, grads: np.ndarray, embs: np.ndarray):
      assert grads.shape == embs.shape
  
      # dot product of gradients and embeddings
      # <float32>[num_tokens]
      grad_dot_input = np.sum(grads * embs, axis=-1)
      scores = citrus_utils.normalize_scores(grad_dot_input)
      return scores
  
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 GradientDotInput obj's underlying member method code with example usage: obj._interpret(grads, embs) and returns: scores