code: def __call__(
      self,
      input_ids,
      pixel_values,
      attention_mask=None,
      position_ids=None,
      token_type_ids=None,
      params: dict = None,
      dropout_rng: jax.random.PRNGKey = None,
      train: bool = False,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ):
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.return_dict
      )
  
      pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
  
      if position_ids is None:
          position_ids = jnp.broadcast_to(
              jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
              input_ids.shape,
          )
  
      if token_type_ids is None:
          token_type_ids = jnp.zeros_like(input_ids)
  
      if attention_mask is None:
          attention_mask = jnp.ones_like(input_ids)
  
      # Handle any PRNG if needed
      rngs = {}
      if dropout_rng is not None:
          rngs["dropout"] = dropout_rng
  
      return self.module.apply(
          {"params": params or self.params},
          jnp.array(input_ids, dtype="i4"),
          jnp.array(pixel_values, dtype=jnp.float32),
          jnp.array(attention_mask, dtype="i4"),
          jnp.array(position_ids, dtype="i4"),
          jnp.array(token_type_ids, dtype="i4"),
          not train,
          output_attentions,
          output_hidden_states,
          return_dict,
          rngs=rngs,
      )
  
imports: from typing import Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
  from flax.traverse_util import flatten_dict, unflatten_dict
  from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring
  from ...utils import add_start_docstrings, logging
  from ..auto.configuration_auto import AutoConfig
  from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel
  from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel
  from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
label_desc: Use image node_id 3 for calling the FlaxVisionTextDualEncoderModel obj's underlying member method code with example usage: obj.__call__(input_ids, pixel_values, attention_mask, position_ids, token_type_ids, params, dropout_rng, train, output_attentions, output_hidden_states, return_dict) and returns: self