code: def initialize_model(
      *,
      model_def: nn.Module,
      input_spec_dict: Dict[str, Tuple[Tuple[int, ...], jnp.dtype]],
      config: ml_collections.ConfigDict,
      rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],
      is_train: Optional[bool] = False,
  ) -> Tuple[PyTree, PyTree, int, Optional[float]]:
      """Initializes parameters and model state.
  
      Args:
        model_def: Definition of a model.
        input_spec_dict: A dictionary of modality_name: (shape, dtype) specifying
          the shape and dtype of the inputs. If unspecified the dtype is float32.
        config: Configurations of the initialization.
        rngs: Jax rng keys.
        is_train: If the scenic model should be initialized in the train mode.
  
      Returns:
        Initial params, Init model_state, and number of trainable_params.
      """
      batch_size = (
          (config.batch_size // jax.device_count())
          if config.get("batch_size")
          else None
      )
      dummy_input = {}
  
      for key, input_spec in input_spec_dict.items():
          in_st = debug_utils.input_spec_to_jax_shape_dtype_struct(
              input_spec, batch_size=batch_size
          )
          dummy_input[key] = jnp.zeros(in_st.shape, in_st.dtype)
  
      # We want all parameters to be created in host RAM, not on any device, they'll
      # be sent there later as needed, otherwise we already encountered two
      # situations where we allocate them twice.
      @functools.partial(jax.jit, backend="cpu")
      def _initialize_model(rngs):
          """Initialization function to be jitted."""
          init_model_state, init_params = flax.core.pop(
              model_def.init(
                  rngs, dummy_input, train=is_train, debug=False
              ),
              "params",
          )
          # Set bias in the head to low value, such that loss is small initially.
          if config.get("init_head_bias", None) is not None:
              init_params = flax.core.unfreeze(init_params)
              for key in input_spec_dict.keys():
                  init_params[
                      f"output_projection_{key}"
                  ] = optimizers.tree_map_with_names(  # pylint: disable=line-too-long
                      lambda p: jnp.full_like(p, config.init_head_bias),
                      init_params[f"output_projection_{key}"],
                      match_name_fn=lambda name: "bias" in name,
                  )
              init_params = flax.core.freeze(init_params)
          return init_params, init_model_state
  
      if not isinstance(rngs, dict):
          rngs = {"params": rngs}
      init_params, init_model_state = _initialize_model(rngs)
      # Pop out params rng:
      rngs.pop("params")
  
      # Count number of trainable parameters:
      num_trainable_params = debug_utils.log_param_shapes(init_params)
      gflops = None  # TODO(lgeorgescu): count the gflops.
  
      return init_params, init_model_state, num_trainable_params, gflops
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Mapping, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.av_mae import base_model
  from scenic.projects.av_mae import train_utils
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_model() and returns: init_params,  init_model_state,  num_trainable_params,  gflops, init_params,  init_model_state