class_docstr: Model provides an Estimator-like API similar to Keras.
code: def init_step(
      self: M,
      key: jnp.ndarray,
      inputs: tp.Any,
  ) -> M:
      model: M = self
  
      if model.module is not None:
          model.module = model.module.init(key, inputs=inputs)
  
      if model.optimizer is not None:
          params = model.parameters()
          model.optimizer = model.optimizer.init(params)
  
      losses, metrics = model._losses_and_metrics.value
      aux_losses = model.loss_logs()
      aux_metrics = model.metric_logs()
  
      model.loss_and_logs = tx.LossAndLogs(
          losses=losses,
          metrics=metrics,
          aux_losses=aux_losses,
          aux_metrics=aux_metrics,
      )
  
      model = model.distributed_strategy.handle_post_init(model)
  
      return model
  
imports: import typing
  import flax
  import jax
  import jax.numpy
  import numpy
  import treex
  from optax import GradientTransformation
  from elegy import types, utils
  from elegy.model.model_base import ModelBase
  from elegy.model.model_core import GradStepOutput, PredStepOutput, TestStepOutput, TrainStepOutput
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.init_step(key, inputs) and returns: model