code: def initialize_model_with_pytree(
      *,
      model_def: nn.Module,
      input_spec: PyTree,
      config: ml_collections.ConfigDict,
      rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],
  ) -> Tuple[PyTree, PyTree, int, Optional[float]]:
      """Initializes parameters and model state with a pytree input_spec.
  
      This function is branched from scenic/train_lib/train_utils.py. Here, the
      model function takes two additional args, `task` and `dataset`.
  
      Args:
        model_def: Definition of a model.
        input_spec: A PyTree whose leaves are (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.
      """
      batch_size = (
          (config.batch_size // jax.device_count())
          if config.get("batch_size")
          else None
      )
  
      def check_leaf_spec(spec: Sequence[PyTree]) -> bool:
          return (
              len(spec) == 2
              and isinstance(spec[0], collections.Sequence)
              and all(isinstance(i, int) for i in spec[0])
              and isinstance(spec[1], jnp.dtype)
          ) or (all(isinstance(i, int) for i in spec[0]))
  
      def create_dummy_input(spec: PyTree) -> PyTree:
          if isinstance(spec, dict):
              return {k: create_dummy_input(v) for k, v in spec.items()}
          elif isinstance(spec, collections.Sequence):
              if check_leaf_spec(spec):
                  in_st = (
                      debug_utils.input_spec_to_jax_shape_dtype_struct(
                          spec, batch_size=batch_size
                      )
                  )
                  return jnp.zeros(in_st.shape, in_st.dtype)
              else:
                  return tuple(
                      create_dummy_input(child) for child in spec
                  )
          elif spec is None:
              return None
          else:
              raise NotImplementedError(
                  "Unsupported spec type.", type(spec)
              )
  
      dummy_input = create_dummy_input(input_spec)
  
      # 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."""
          # If dummy_input is a dict, we feed inputs as keyword arguments, otherwise
          # feed as position arguments.
          if isinstance(dummy_input, dict):
              init_model_state, init_params = flax.core.pop(
                  flax.core.freeze(
                      model_def.init(
                          rngs,
                          **dummy_input,
                          task=config.dataset_configs.task,
                          dataset=config.dataset_configs.get(
                              "name", ""
                          ),
                          train=False,
                          debug=False,
                      )
                  ),
                  "params",
              )
          else:
              init_model_state, init_params = flax.core.pop(
                  flax.core.freeze(
                      model_def.init(
                          rngs,
                          *dummy_input,
                          task=config.dataset_configs.task,
                          dataset=config.dataset_configs.get(
                              "name", ""
                          ),
                          train=False,
                          debug=False,
                      )
                  ),
                  "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_with_pytree(
              flax_model_apply_fn=functools.partial(
                  model_def.apply,
                  variables,
                  task=config.dataset_configs.task,
                  dataset=config.dataset_configs.get("name", ""),
                  train=False,
                  debug=False,
                  rngs=rngs,
              ),
              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 functools
  import re
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  import flax
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import ml_collections
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.clip import model
  from scenic.projects.unloc import eval_utils
  from scenic.projects.unloc import model_utils
  from scenic.projects.vivit import model_utils
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  import scipy.ndimage
label_desc: Use image node_id 9 for calling a global function with example usage: initialize_model_with_pytree() and returns: init_params,  init_model_state,  num_trainable_params,  gflops, unknown, init_params,  init_model_state, unknown, jnp, tuple, None