class_docstr: Autoregressive sequence model utilizing beam search.
  
  * batch_shape -> Given by initializer
  * event_shape -> N x T sequence of choices
  
  Parameters:
      model (AutoregressiveModel): A lazily computed autoregressive model.
      init (tuple of tensors, batch_shape x ...): initial state of autoregressive model.
      n_classes (int): number of classes in each time step
      n_length (int): max length of sequence
class_docstr_tok: ["Autoregressive","sequence","model","utilizing","beam","search",".","*","batch_shape","-",">","Given","by","initializer","*","event_shape","-",">","N","x","T","sequence","of","choices","Parameters",":","model","(","AutoregressiveModel",")",":","A","lazily","computed","autoregressive","model",".","init","(","tuple","of","tensors",",","batch_shape","x","...",")",":","initial","state","of","autoregressive","model",".","n_classes","(","int",")",":","number","of","classes","in","each","time","step","n_length","(","int",")",":","max","length","of","sequence"]
code: def _beam_max(self, K):
      return self._beam_search(KMaxSemiring(K))[1]
  
file_path: pytorch-struct/torch_struct/autoregressive.py
imports: import torch
  from .semirings import MaxSemiring, KMaxSemiring, TempMax
  from torch.distributions.distribution import Distribution
label_desc: Use image node_id 7 for calling the Autoregressive obj's underlying member method code with example usage: obj._beam_max(K) and returns: self