code: def initialize_multitask_model(
      *,
      model_def: nn.Module,
      input_spec: Dict[
          Tuple[Tuple[str, Any], ...],
          Sequence[
              Union[Tuple[Tuple[int, ...], jnp.dtype], Tuple[int, ...]]
          ],
      ],
      config: ml_collections.ConfigDict,
      rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],
  ) -> Tuple[PyTree, PyTree, int, Optional[Dict[str, float]]]:
      """Initializes parameters and model state.
  
      Args:
        model_def: Definition of a model.
        input_spec: A dictionary from a dict of keyword arguments to an iterable of
          (shape, dtype) pairs specifying the shape and dtype of the inputs. If
          unspecified the dtype is float32.
        config: Configurations of the initialization.
        rngs: Jax rng keys.
  
      Returns:
        Initial params, Init model_state, and number of trainable_params.
      """
  
      def init_fn(model_def):
          for kwargs, in_spec in input_spec.items():
              if config.get("batch_sizes") is not None:
                  batch_size = config.batch_sizes.get(
                      dict(kwargs)["dataset"]
                  )
              else:
                  batch_size = config.batch_size
  
              batch_size = (
                  (batch_size // jax.device_count())
                  if batch_size
                  else None
              )
  
              input_shapetype = [
                  debug_utils.input_spec_to_jax_shape_dtype_struct(
                      spec, batch_size=batch_size
                  )
                  for spec in in_spec
              ]
              dummy_input = []
              for in_st in input_shapetype:
                  dummy_input.append(
                      jnp.zeros(in_st.shape, in_st.dtype)
                  )
              model_def(
                  *dummy_input, train=False, debug=False, **dict(kwargs)
              )
  
      # 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(
              flax.core.freeze(
                  nn.init(fn=init_fn, module=model_def)(rngs)
              ),
              "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
              and "output_projection" in init_params
          ):
              init_params = flax.core.unfreeze(init_params)
              init_params[
                  "output_projection"
              ] = optimizers.tree_map_with_names(
                  lambda p: jnp.full_like(p, config.init_head_bias),
                  init_params["output_projection"],
                  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)
  
      # Count gflops:
      count_flops = config.get(
          "count_flops", ml_collections.ConfigDict()
      )
      if count_flops:
          variables = {"params": init_params, **init_model_state}
          gflops_dict = {}
          gflops_all = 0
          for kwargs, in_spec in input_spec.items():
              flops = debug_utils.compute_flops(
                  flax_model_apply_fn=functools.partial(
                      model_def.apply,
                      variables,
                      train=False,
                      debug=False,
                      rngs=rngs,
                      **dict(kwargs),
                  ),
                  input_spec=count_flops.get("input_spec", in_spec),
                  fuse_multiply_add=count_flops.get(
                      "fuse_multiply_add", True
                  ),
              )
              gflops = flops / (10**9)
              gflops_key = "gflops/" + "/".join(
                  f"{x}={y}" for x, y in kwargs
              )
              gflops_dict[gflops_key] = gflops
              gflops_all += gflops
          gflops_dict["gflops"] = gflops_all
      else:
          gflops_dict = None
  
      return (
          init_params,
          init_model_state,
          num_trainable_params,
          gflops_dict,
      )
  
imports: import collections.abc
  import copy
  import functools
  import os
  import re
  import time
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax import struct
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.train_lib import optimizers
  from tensorflow.io import gfile
label_desc: Use image node_id 4 for calling a global function with example usage: initialize_multitask_model() and returns: init_params,  init_model_state,  num_trainable_params,  gflops_dict, init_params,  init_model_state