class_docstr: **Transformer** 的 Decoder
  
  :param embed: ``decoder`` 输入的 embedding，支持以下几种输入类型：
  
          - ``tuple(num_embedings, embedding_dim)``，即 embedding 的大小和每个词的维度，此时将随机初始化一个 :class:`torch.nn.Embedding` 实例；
          - :class:`torch.nn.Embedding` 或 **fastNLP** 的 ``Embedding`` 对象，此时就以传入的对象作为 embedding；
          - :class:`numpy.ndarray` ，将使用传入的 ndarray 作为 Embedding 初始化；
          - :class:`torch.Tensor`，此时将使用传入的值作为 Embedding 初始化；
  :param pos_embed: 位置 embedding
  :param d_model: 输入、输出的维度
  :param num_layers: :class:`TransformerSeq2SeqDecoderLayer` 的层数
  :param n_head: **多头注意力** head 的数目，需要能被 ``d_model`` 整除
  :param dim_ff: FFN 中间映射的维度
  :param dropout: :class:`~fastNLP.modules.torch.decoder.SelfAttention` 和 FFN 中的 dropout 的大小
  :param bind_decoder_input_output_embed: ``decoder`` 的输出 embedding 是否与其输入 embedding 是一样的权重（即为同一个），若 ``embed`` 为 
      :class:`~fastNLP.embeddings.StaticEmbedding`，则 ``StaticEmbedding`` 的 ``vocab`` 不能包含 ``no_create_entry`` 的 token ，同时
      ``StaticEmbedding`` 初始化时 ``lower`` 为 ``False``，``min_freq=1``。
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docstr: 初始化一个 :class:`~fastNLP.modules.torch.decoder.TransformerState`` 用于 :meth:`forward`
  
  :param encoder_output: ``encoder`` 的输出，形状为 ``[batch_size, max_len, d_model]``
  :param encoder_mask: ``[batch_size, max_len]]``，为 **0** 的位置是 padding, 用来指示输入中哪些不需要 attend 。
  :return:
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code: def init_state(
      self,
      encoder_output: torch.FloatTensor,
      encoder_mask: torch.ByteTensor,
  ) -> TransformerState:
      """
      初始化一个 :class:`~fastNLP.modules.torch.decoder.TransformerState`` 用于 :meth:`forward`
  
      :param encoder_output: ``encoder`` 的输出，形状为 ``[batch_size, max_len, d_model]``
      :param encoder_mask: ``[batch_size, max_len]]``，为 **0** 的位置是 padding, 用来指示输入中哪些不需要 attend 。
      :return:
      """
      if isinstance(encoder_output, torch.Tensor):
          encoder_output = encoder_output
      elif isinstance(encoder_output, (list, tuple)):
          encoder_output = encoder_output[0]  # 防止是LSTMEncoder的输出结果
      else:
          raise TypeError(
              "Unsupported `encoder_output` for TransformerSeq2SeqDecoder"
          )
      state = TransformerState(
          encoder_output,
          encoder_mask,
          num_decoder_layer=self.num_layers,
      )
      return state
  
imports: from typing import Union, Tuple
  import math
  import torch
  from torch import nn
  import torch.nn.functional
  from ..attention import AttentionLayer, MultiHeadAttention
  from ....embeddings.torch.utils import get_embeddings
  from ....embeddings.torch.static_embedding import StaticEmbedding
  from .seq2seq_state import State, LSTMState, TransformerState
label_desc: Use image node_id 3 for calling the TransformerSeq2SeqDecoder obj's underlying member method code with example usage: obj.init_state(encoder_output, encoder_mask) and returns: state