code: def get_labels_verbs(
      inners: jnp.ndarray, batch_mask_verb: jnp.ndarray
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Labels the inner products computed from `compute_inners` for verb loss."""
      bs = inners.shape[0]
      labels = jnp.zeros(
          inners.reshape((bs, bs, -1)).shape, dtype=inners.dtype
      )
      for i in range(inners.shape[0]):
          labels = labels.at[i, i, 0].set(
              jnp.squeeze(batch_mask_verb[i])
          )
      masking = jnp.ones(
          batch_mask_verb.shape, dtype=batch_mask_verb.dtype
      )
      masking = masking - batch_mask_verb
      masking = jnp.transpose(jnp.tile(masking, bs)) * -1000000
      return labels.reshape(inners.shape), masking
  
file_path: scenic/scenic/projects/verbs_in_action/losses.py
imports: from typing import Tuple
  import flax.linen
  import jax.numpy
  import numpy
  from scenic.projects.verbs_in_action import utils
label_desc: Use image node_id 7 for calling a global function with example usage: get_labels_verbs(inners, batch_mask_verb) and returns: labels,  masking