class_docstr: MBOP Builder.
  
  This builder uses ensemble regressor learners for the world model, policy
  prior and the n-step return models with fixed learning rates. The ensembles
  and the learning rate are configured in the config.
docstr: Initializes an MBOP builder.
  
  Args:
    config: a config with MBOP hyperparameters.
    losses: MBOP losses.
    mean_std: NestedMeanStd used to normalize the samples.
docstr_tok: ["Initializes","an","MBOP","builder",".","Args",":","config",":","a","config","with","MBOP","hyperparameters",".","losses",":","MBOP","losses",".","mean_std",":","NestedMeanStd","used","to","normalize","the","samples","."]
code: def __init__(
      self,
      config: mbop_config.MBOPConfig,
      losses: mbop_losses.MBOPLosses,
      mean_std: Optional[running_statistics.NestedMeanStd] = None,
  ):
      """Initializes an MBOP builder.
  
      Args:
        config: a config with MBOP hyperparameters.
        losses: MBOP losses.
        mean_std: NestedMeanStd used to normalize the samples.
      """
      self._config = config
      self._losses = losses
      self._mean_std = mean_std
  
imports: import functools
  from typing import Iterator, Optional
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import builders
  from acme.agents.jax.mbop import acting
  from acme.agents.jax.mbop import config
  from acme.agents.jax.mbop import learning
  from acme.agents.jax.mbop import losses
  from acme.agents.jax.mbop import networks
  from acme.jax import networks
  from acme.jax import running_statistics
  from acme.utils import counting
  from acme.utils import loggers
  import optax
label_desc: Use image node_id 1 to create a new MBOPBuilder object from inherited base classes: builders.OfflineBuilder with example: obj = MBOPBuilder(config, losses, mean_std)