docstr: Args:
      input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`):
          Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
          provide it.
  
          Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
          [`PreTrainedTokenizer.__call__`] for details.
  
          [What are input IDs?](../glossary#input-ids)
  
  Returns:
      text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
      the projection layer to the pooled output of text model.
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code: def get_text_features(
      self,
      input_ids,
      attention_mask=None,
      position_ids=None,
      token_type_ids=None,
      params: dict = None,
      dropout_rng: jax.random.PRNGKey = None,
      train=False,
  ):
      r"""
      Args:
          input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
              provide it.
  
              Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
  
      Returns:
          text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
          the projection layer to the pooled output of text model.
      """
      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
  
      def _get_features(
          module,
          input_ids,
          attention_mask,
          position_ids,
          token_type_ids,
          deterministic,
      ):
          text_outputs = module.text_model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              position_ids=position_ids,
              token_type_ids=token_type_ids,
              deterministic=deterministic,
          )
          pooled_output = text_outputs[1]
          text_features = module.text_projection(pooled_output)
          return text_features
  
      return self.module.apply(
          {"params": params or self.params},
          jnp.array(input_ids, dtype="i4"),
          jnp.array(attention_mask, dtype="i4"),
          jnp.array(position_ids, dtype="i4"),
          jnp.array(token_type_ids, dtype="i4"),
          not train,
          method=_get_features,
          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 4 for calling the FlaxVisionTextDualEncoderModel obj's underlying member method code with example usage: obj.get_text_features(input_ids, attention_mask, position_ids, token_type_ids, params, dropout_rng, train) and returns: self, text_features