class_docstr: Weighted cross-entropy loss for a sequence of logits.
code: def __init__(
      self,
      average_across_timesteps: bool = False,
      average_across_batch: bool = False,
      sum_over_timesteps: bool = True,
      sum_over_batch: bool = True,
      softmax_loss_function: Optional[Callable] = None,
      name: Optional[str] = None,
  ):
      super().__init__(
          reduction=tf.keras.losses.Reduction.NONE, name=name
      )
      self.average_across_timesteps = average_across_timesteps
      self.average_across_batch = average_across_batch
      self.sum_over_timesteps = sum_over_timesteps
      self.sum_over_batch = sum_over_batch
      self.softmax_loss_function = softmax_loss_function
  
file_path: addons/tensorflow_addons/seq2seq/loss.py
imports: import tensorflow
  from tensorflow_addons.utils.types import TensorLike
  from typeguard import typechecked
  from typing import Callable, Optional
label_desc: Use image node_id 1 to create a new SequenceLoss object from inherited base classes: tf.keras.losses with example: obj = SequenceLoss(average_across_timesteps, average_across_batch, sum_over_timesteps, sum_over_batch, softmax_loss_function, name)