class_docstr: Text-query-based OWL-ViT model.
  
  This module computes joint text and image embeddings which are then
  used for localized prediction of bounding boxes and classes.
  
  Attributes:
    body_configs: Configurations of the image-text module.
    objectness_head_configs: Configurations for the (optional) objectness head.
    mask_head_configs: Configurations for the (optional) mask head.
    normalize: Whether to normalize the output of the model and the
      label_embeddings before computing the class logits.
    box_bias: Type of box bias - one of 'location', 'size' or 'both'.
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docstr: Embeds text into features.
  
  Args:
    text_queries: jnp.int32 tokenized text queries of shape [..., num_tokens].
    train: Whether or not we are in training mode.
  
  Returns:
    An array of the same shape as text_queries, except for the last dimension,
    which is num_dimensions instead of num_tokens.
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code: def text_embedder(
      self, text_queries: jnp.ndarray, train: bool
  ) -> jnp.ndarray:
      """Embeds text into features.
  
      Args:
        text_queries: jnp.int32 tokenized text queries of shape [..., num_tokens].
        train: Whether or not we are in training mode.
  
      Returns:
        An array of the same shape as text_queries, except for the last dimension,
        which is num_dimensions instead of num_tokens.
      """
      _, text_features = self._embedder(texts=text_queries, train=train)
      return text_features
  
imports: import copy
  from typing import Any, Dict, List, Mapping, Optional
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  from scenic.projects.owl_vit import layers
  from scenic.projects.owl_vit import matching_base_models
  from scenic.projects.owl_vit import utils
  from scenic.projects.owl_vit.clip import model
  from scenic.projects.owl_vit.clip import tokenizer
label_desc: Use image node_id 9 for calling the TextZeroShotDetectionModule obj's underlying member method code with example usage: obj.text_embedder(text_queries, train) and returns: text_features