class_docstr: Wraps an OWL-ViT FLAX model for convenient inference.
  
  All public methods apply to a single example and take and return Numpy arrays.
  
  Attributes:
    config: ConfigDict with model configuration.
    module: OWL-ViT Flax module.
    variables: Variable dict to be used with module.apply.
docstr: Embeds text queries.
  
  Args:
    queries: Tuple of query strings.
  
  Returns:
    Numpy arrays containing query embeddings.
docstr_tok: ["Embeds","text","queries",".","Args",":","queries",":","Tuple","of","query","strings",".","Returns",":","Numpy","arrays","containing","query","embeddings","."]
code: def embed_text_queries(self, queries: Tuple[str, ...]) -> np.ndarray:
      """Embeds text queries.
  
      Args:
        queries: Tuple of query strings.
  
      Returns:
        Numpy arrays containing query embeddings.
      """
      tokenized = np.array(
          [
              self.module.apply(
                  self.variables, q, method=self.module.tokenize
              )
              for q in queries
          ]
      )
      # Pad queries to avoid re-compilation:
      n = len(queries)
      num_pad = (
          int(np.ceil(n / QUERY_PAD_BIN_SIZE) * QUERY_PAD_BIN_SIZE) - n
      )
      tokenized = tf.pad(tokenized, [[0, num_pad], [0, 0]]).numpy()
      return np.array(self._embed_texts_jitted(tokenized[None, ...])[0])
  
imports: import dataclasses
  import functools
  from typing import Any, Dict, Tuple
  from flax import linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models import box_utils
  from scenic.projects.owl_vit.notebooks import numpy_cache
  from scipy import special
  from skimage import transform
  import tensorflow
label_desc: Use image node_id 6 for calling the Model obj's underlying member method code with example usage: obj.embed_text_queries(queries) and returns: np