code: def _step(self) -> bool:
      # Generate the next step output.
      model_output = self._decode(
          self.seqs[:, self.step_nr - 1 : self.step_nr]
      )
  
      self.state_bag.increment_step_nr()
  
      logits = model_output.logits
  
      if self.temperature != 1.0:
          logits /= self.temperature
  
      # (N, 1, V)
      probs = softmax(logits, dim=-1, dtype=torch.float32)
  
      # (N, 1, V) -> (N, V)
      probs.squeeze_(1)
  
      if probs.isnan().any():
          raise RuntimeError(
              f"The model has produced one or more NaN probabilities at step {self.step_nr}. The sequence generator cannot continue."
          )
  
      # If we are generating the last possible step, force it to be EOS
      # regardless of its score.
      if self.step_nr == self.max_seq_len - 1:
          batch_size = self.seqs.size(0)
  
          # (N)
          vocab_indices = self.seqs.new_full(
              (batch_size,), self.eos_idx
          )
      else:
          # Process `probs` in-place if requested.
          for processor in self.step_processors:
              processor(self.seqs[:, : self.step_nr], probs)
  
          # Apply UNK penalty.
          if self.unk_idx is not None:
              probs[:, self.unk_idx] -= self.unk_penalty
  
          # Never allow PAD.
          if self.pad_idx is not None:
              probs[:, self.pad_idx] = 0
  
          # Do not allow EOS till we reach the minimum sequence length.
          if self.step_nr < self.min_seq_len - 1:
              probs[:, self.eos_idx] = 0
  
          # (N)
          vocab_indices = self.sampler(probs)
  
      # EOS mask of the current step.
      # (N)
      eos_mask = vocab_indices == self.eos_idx
  
      # Ignore the generated indices for the prompt sequences.
      if self.step_nr < self.max_prompt_len:
          assert self.prompt_mask is not None
  
          # (N)
          mask = self.prompt_mask[:, self.step_nr]
  
          # Override the generated indices.
          vocab_indices[mask] = self.seqs[mask, self.step_nr]
  
          # Ignore EOS in the prompt sequences.
          eos_mask[mask] = False
      else:
          self.prompt_mask = None  # Not needed anymore, release.
  
      # Record the current step.
      self.seqs[:, self.step_nr] = vocab_indices
  
      if self.step_scores is not None:
          # (N, 1)
          scores = torch.gather(
              probs, dim=-1, index=vocab_indices[:, None]
          )
  
          # Record the scores of the current step.
          self.step_scores[:, self.step_nr] = scores.squeeze(1)
  
      if self.step_hooks:
          seqs = self.seqs[:, : self.step_nr + 1]
  
          if self.step_scores is None:
              step_scores = None
          else:
              step_scores = self.step_scores[:, : self.step_nr + 1]
  
          for hook in self.step_hooks.values():
              hook(
                  self.prompt_indices, seqs, step_scores, prefill=False
              )
  
      # Retrieve the indices of the sequences that have reached EOS.
      # (F, 1)
      eos_seq_indices = eos_mask.nonzero()
  
      # If one or more sequences have reached EOS, move them to the output and
      # continue generating the remaining sequences.
      if len(eos_seq_indices) > 0:
          # Move the sequences that have reached EOS to the output.
          for seq_idx in eos_seq_indices:
              self._finish_sequence(int(seq_idx))
  
          # (N)
          active_seq_mask = ~eos_mask
  
          # (N - F, 1) -> (N - F)
          active_seq_indices = active_seq_mask.nonzero().squeeze(-1)
  
          # No sequence left, we can return.
          if len(active_seq_indices) == 0:
              return False
  
          # Otherwise, remove the sequences that have reached EOS from the
          # state and continue generating the remaining ones.
          self._reorder_state(active_seq_indices)
  
      return True
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Dict, List, Optional, Sequence, Tuple, Union, final
  import torch
  from torch import Tensor
  from torch.nn.functional import softmax
  from fairseq2.data import VocabularyInfo
  from fairseq2.generation.generator import Hypothesis, Seq2SeqGenerator, Seq2SeqGeneratorOutput, SequenceGenerator, SequenceGeneratorOutput, StepHook
  from fairseq2.generation.step_processor import StepProcessor
  from fairseq2.models.decoder import DecoderModel
  from fairseq2.models.encoder_decoder import EncoderDecoderModel
  from fairseq2.models.sequence import SequenceModelOutput
  from fairseq2.nn.incremental_state import IncrementalStateBag
  from fairseq2.nn.ops import repeat_interleave
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.typing import finaloverride, override
label_desc: Use image node_id 5 for calling the _SamplingSequenceGeneratorOpBase obj's underlying member method code with example usage: obj._step() and returns: True, False