class_docstr: General Task for Sequence2Sequence.
  
  Args:
      max_source_length: The maximum length to pad / truncate input sequences to.
      max_target_length: The maximum length to pad / truncate target sequences to.
      padding: The type of padding to apply. One of: "longest" or ``True``, "max_length", "do_not_pad" or
          ``False``.
      loss_fn: Loss function for training
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      metrics: Metrics to compute for training and evaluation. Changing this argument currently has no effect
      learning_rate: Learning rate to use for training, defaults to `3e-4`
      num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`
      enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
code: def forward(self, x: Any) -> Any:
      max_length = self.max_target_length
      num_beams = (
          self.num_beams
          if self.num_beams
          else self.model.config.num_beams
      )
      generated_tokens = self.model.generate(
          input_ids=x["input_ids"],
          attention_mask=x.get("attention_mask", None),
          max_length=max_length,
          num_beams=num_beams,
      )
      # in case the batch is shorter than max length, the output should be padded
      if generated_tokens.shape[-1] < max_length:
          generated_tokens = _pad_tensors_to_max_len(
              model_cfg=self.model.config,
              tensor=generated_tokens,
              max_length=max_length,
          )
      return generated_tokens
  
imports: import os
  import warnings
  from typing import Any, Dict, Iterable, List, Optional, Type, Union
  import torch
  from pytorch_lightning import Callback
  from pytorch_lightning.utilities import rank_zero_info
  from torch import Tensor
  from torch.nn import Module
  from flash.core.data.io.input import DataKeys, ServeInput
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.data.io.output import Output
  from flash.core.data.io.output_transform import OutputTransform
  from flash.core.model import Task
  from flash.core.registry import ExternalRegistry, FlashRegistry
  from flash.core.serve import Composition
  from flash.core.utilities.imports import _TOPIC_TEXT_AVAILABLE, requires
  from flash.core.utilities.providers import _HUGGINGFACE
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE, LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
  from flash.text.input import TextDeserializer
  from flash.text.ort_callback import ORTCallback
  from flash.text.seq2seq.core.collate import TextSeq2SeqCollate
label_desc: Use image node_id 2 for calling the Seq2SeqTask obj's underlying member method code with example usage: obj.forward(x) and returns: generated_tokens