code: def init_weights(
      self,
      rng: jax.random.PRNGKey,
      input_shape: Tuple,
      params: FrozenDict = None,
  ) -> FrozenDict:
      # init input tensor
      input_ids = jnp.zeros(input_shape[0], dtype="i4")
      position_ids = jnp.broadcast_to(
          jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
          input_shape[0],
      )
      token_type_ids = jnp.ones_like(input_ids)
      attention_mask = jnp.ones_like(input_ids)
  
      pixel_values = jax.random.normal(rng, input_shape[1])
  
      params_rng, dropout_rng = jax.random.split(rng)
      rngs = {"params": params_rng, "dropout": dropout_rng}
  
      return self.module.init(
          rngs,
          input_ids,
          pixel_values,
          attention_mask,
          position_ids,
          token_type_ids,
      )["params"]
  
imports: from typing import Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  from configuration_hybrid_clip import HybridCLIPConfig
  from flax.core.frozen_dict import FrozenDict
  from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
  from transformers.modeling_flax_utils import FlaxPreTrainedModel
  from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling the FlaxHybridCLIP obj's underlying member method code with example usage: obj.init_weights(rng, input_shape, params) and returns: self