class_docstr: Represents a sequence-to-sequence batch.
class_docstr_tok: ["Represents","a","sequence-to-sequence","batch","."]
docstr: Use this batch for model training or validation.
  
  :returns:
    - A new batch with the target sequences trimmed one step from the end
      to use as model input.
    - The target sequences trimmed one step from the beginning to use in
      loss computation.
docstr_tok: ["Use","this","batch","for","model","training","or","validation",".",":","returns",":","-","A","new","batch","with","the","target","sequences","trimmed","one","step","from","the","end","to","use","as","model","input",".","-","The","target","sequences","trimmed","one","step","from","the","beginning","to","use","in","loss","computation","."]
code: def as_input_and_target(self) -> Tuple[Seq2SeqBatch, Tensor]:
      """Use this batch for model training or validation.
  
      :returns:
        - A new batch with the target sequences trimmed one step from the end
          to use as model input.
        - The target sequences trimmed one step from the beginning to use in
          loss computation.
      """
      if (target_seq_len := self.target_seqs.size(1)) < 2:
          raise ValueError(
              f"The sequence length of `target_seqs` must be at least 2 for training, but is {target_seq_len} instead."
          )
  
      target_seqs = self.target_seqs[:, :-1]
  
      if self.target_padding_mask is None:
          target_padding_mask = None
      else:
          target_padding_mask = self.target_padding_mask.trim(1)
  
      batch = Seq2SeqBatch(
          self.source_seqs,
          self.source_padding_mask,
          target_seqs,
          target_padding_mask,
      )
  
      return batch, self.target_seqs[:, 1:]
  
imports: from __future__ import annotations
  import math
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Any, Optional, Sequence, Tuple, final
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torcheval.metrics import Mean, Sum, Throughput
  from fairseq2.data import VocabularyInfo
  from fairseq2.gang import Gang
  from fairseq2.metrics import MetricBag
  from fairseq2.models.sequence import SequenceModelOutput
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.typing import finaloverride
label_desc: Use image node_id 1 for calling the Seq2SeqBatch obj's underlying member method code with example usage: obj.as_input_and_target() and returns: batch,  self