class_docstr: Encoder decoder models with various lattice losses.
class_docstr_tok: ["Encoder","decoder","models","with","various","lattice","losses","."]
docstr: Forward pass of the model.
  
  Args:
      input_signal: Tensor that represents a batch of raw audio signals,
          of shape [B, T]. T here represents timesteps, with 1 second of audio represented as
          `self.sample_rate` number of floating point values.
      input_signal_length: Vector of length B, that contains the individual lengths of the audio
          sequences.
      processed_signal: Tensor that represents a batch of processed audio signals,
          of shape (B, D, T) that has undergone processing via some DALI preprocessor.
      processed_signal_length: Vector of length B, that contains the individual lengths of the
          processed audio sequences.
  
  Returns:
      A tuple of 3 elements -
      1) The log probabilities tensor of shape [B, T, D].
      2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B].
      3) The greedy token predictions of the model of shape [B, T] (via argmax)
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code: def forward(
      self,
      input_signal=None,
      input_signal_length=None,
      processed_signal=None,
      processed_signal_length=None,
  ):
      """
      Forward pass of the model.
  
      Args:
          input_signal: Tensor that represents a batch of raw audio signals,
              of shape [B, T]. T here represents timesteps, with 1 second of audio represented as
              `self.sample_rate` number of floating point values.
          input_signal_length: Vector of length B, that contains the individual lengths of the audio
              sequences.
          processed_signal: Tensor that represents a batch of processed audio signals,
              of shape (B, D, T) that has undergone processing via some DALI preprocessor.
          processed_signal_length: Vector of length B, that contains the individual lengths of the
              processed audio sequences.
  
      Returns:
          A tuple of 3 elements -
          1) The log probabilities tensor of shape [B, T, D].
          2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B].
          3) The greedy token predictions of the model of shape [B, T] (via argmax)
      """
      log_probs, encoded_len, greedy_predictions = super().forward(
          input_signal=input_signal,
          input_signal_length=input_signal_length,
          processed_signal=processed_signal,
          processed_signal_length=processed_signal_length,
      )
      return self._forward_k2_post_processing(
          log_probs=log_probs,
          encoded_length=encoded_len,
          greedy_predictions=greedy_predictions,
      )
  
imports: from typing import List, Optional
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE
  from nemo.collections.asr.models.ctc_models import EncDecCTCModel
  from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
  from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel
  from nemo.collections.asr.parts.k2.classes import ASRK2Mixin
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the EncDecK2SeqModel obj's underlying member method code with example usage: obj.forward(input_signal, input_signal_length, processed_signal, processed_signal_length) and returns: self