class_docstr: Wraps a world model in serialization machinery for training.
  
  The resulting model takes as input the observation and action sequences,
  serializes them and interleaves into one sequence, which is fed into a given
  autoregressive model. The resulting logit sequence is deinterleaved into
  observations and actions, and the observation logits are returned together
  with computed symbol significance weights.
  
  The model has a signature
  (obs, act, obs, mask) -> (obs_logits, obs_repr, weights), where obs are
  observations (the second occurrence is the target), act are actions, mask is
  the observation mask, obs_logits are logits of the output observation
  representation, obs_repr is the target observation representation and weights
  are the target weights.
class_docstr_tok: ["Wraps","a","world","model","in","serialization","machinery","for","training",".","The","resulting","model","takes","as","input","the","observation","and","action","sequences",",","serializes","them","and","interleaves","into","one","sequence",",","which","is","fed","into","a","given","autoregressive","model",".","The","resulting","logit","sequence","is","deinterleaved","into","observations","and","actions",",","and","the","observation","logits","are","returned","together","with","computed","symbol","significance","weights",".","The","model","has","a","signature","(","obs",",","act",",","obs",",","mask",")","-",">","(","obs_logits",",","obs_repr",",","weights",")",",","where","obs","are","observations","(","the","second","occurrence","is","the","target",")",",","act","are","actions",",","mask","is","the","observation","mask",",","obs_logits","are","logits","of","the","output","observation","representation",",","obs_repr","is","the","target","observation","representation","and","weights","are","the","target","weights","."]
docstr: Extracts the state of the underlying sequence model.
docstr_tok: ["Extracts","the","state","of","the","underlying","sequence","model","."]
code: def seq_model_state(self):
      """Extracts the state of the underlying sequence model."""
      return self.state[2]
  
imports: import functools
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
  from trax.rl import space_serializer
label_desc: Use image node_id 6 for calling the SerializedModel obj's underlying member method code with example usage: obj.seq_model_state() and returns: self