class_docstr: A two-tower image-text encoder model.
class_docstr_tok: ["A","two-tower","image-text","encoder","model","."]
code: def encode_image_and_text_and_do_pretraining(
      self,
      image: jnp.ndarray,  # Shape: (N, H, W, C)
      text: jnp.ndarray,  # Shape: (N, L)
      mask: Optional[jnp.ndarray],  # Shape: (N, L)
      text_for_mlm: jnp.ndarray,
      segment_ids_for_mlm: jnp.ndarray,
      mask_for_mlm: Optional[jnp.ndarray],
      masked_lm_positions: jnp.ndarray,
      *,
      train: bool = False,
      debug: bool = False,
  ) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
      # pylint: disable=not-callable
      encoded_image = self.image_encoder(
          image, train=train, debug=debug
      )
      (
          encoded_text,
          mlm_logits,
      ) = self.text_encoder(  # pytype: disable=wrong-keyword-args
          text,
          mask,
          text_for_mlm=text_for_mlm,
          segment_ids_for_mlm=segment_ids_for_mlm,
          mask_for_mlm=mask_for_mlm,
          masked_lm_positions=masked_lm_positions,
          train=train,
          debug=debug,
      )
      # pylint: enable=not-callable
      return encoded_image, encoded_text, mlm_logits
  
imports: import abc
  from typing import Literal
  from typing import Optional
  import flax
  from flax.core.frozen_dict import FrozenDict
  import flax.linen
  import jax
  import jax.numpy
label_desc: Use image node_id 6 for calling the ImageTextEncoder obj's underlying member method code with example usage: obj.encode_image_and_text_and_do_pretraining(image, text, mask, text_for_mlm, segment_ids_for_mlm, mask_for_mlm, masked_lm_positions) and returns: encoded_image,  encoded_text,  mlm_logits