code: def initialize_model(
      *,
      model_def: nn.Module,
      input_spec: Sequence[
          Union[
              Tuple[Tuple[int, ...], jnp.dtype], Tuple[int, ...], None
          ]
      ],
      config: ml_collections.ConfigDict,
      rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],
      train: Optional[bool] = False,
      **model_kwargs,
  ) -> Tuple[PyTree, PyTree, int, Optional[float]]:
      """Initializes parameters and model state.
  
      Args:
        model_def: Definition of a model.
        input_spec: 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.
        train: If the scenic model should be initialized in the train mode.
        **model_kwargs: Kwargs passed to flax model initialization.
  
      Returns:
        Initial params, Init model_state, number of trainable_params, and gflops.
      """
      batch_size = (
          (config.batch_size // jax.device_count())
          if config.get("batch_size")
          else None
      )
      dummy_input = []
      for spec in input_spec:
          if spec is not None:
              in_st = debug_utils.input_spec_to_jax_shape_dtype_struct(
                  spec, batch_size=batch_size
              )
              dummy_input.append(jnp.zeros(in_st.shape, in_st.dtype))
          else:
              dummy_input.append(None)
  
      # 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(
                  model_def.init(
                      rngs,
                      *dummy_input,
                      train=train,
                      debug=False,
                      **model_kwargs,
                  )
              ),
              "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)
              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({"count_flops": True}),
      )
      if count_flops:
          variables = {"params": init_params, **init_model_state}
          flops = debug_utils.compute_flops(
              flax_model_apply_fn=functools.partial(
                  model_def.apply,
                  variables,
                  train=False,
                  debug=False,
                  rngs=rngs,
                  **model_kwargs,
              ),
              input_spec=count_flops.get("input_spec", input_spec),
              fuse_multiply_add=count_flops.get(
                  "fuse_multiply_add", True
              ),
          )
          gflops = flops / (10**9)
      else:
          gflops = None
  
      return init_params, init_model_state, num_trainable_params, gflops
  
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 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