code: def __init__(
      self,
      num_layers=3,
      units=512,
      hidden_size=2048,
      num_heads=8,
      activation_dropout=0.1,
      dropout=0.1,
      attention_dropout=0.0,
      layernorm_eps=1e-12,
      activation="relu",
      dtype="float32",
      layout="NT",
      pre_norm=False,
      weight_initializer=None,
      bias_initializer=None,
  ):
      super().__init__()
      self.query_k_bias = Parameter(
          "query_k_bias",
          shape=(num_heads, units // num_heads),
          init=bias_initializer,
          allow_deferred_init=True,
      )
      self.query_r_bias = Parameter(
          "query_r_bias",
          shape=(num_heads, units // num_heads),
          init=bias_initializer,
          allow_deferred_init=True,
      )
      self.decoder_layers = nn.HybridSequential()
      for i in range(num_layers):
          self.decoder_layers.add(
              TransformerXLDecoderLayer(
                  units=units,
                  hidden_size=hidden_size,
                  num_heads=num_heads,
                  activation_dropout=activation_dropout,
                  dropout=dropout,
                  attention_dropout=attention_dropout,
                  layer_norm_eps=layernorm_eps,
                  activation=activation,
                  dtype=dtype,
                  layout=layout,
                  pre_norm=pre_norm,
                  weight_initializer=weight_initializer,
                  bias_initializer=bias_initializer,
              )
          )
  
imports: import numpy
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import nn, Block, HybridBlock, Parameter
  from ..attention_cell import multi_head_dot_attn, gen_self_attn_mask, gen_mem_attn_mask, RelAttentionScoreCell, MultiHeadAttentionCell
  from ..layers import get_activation, PositionalEmbedding, PositionwiseFFN, AdaptiveEmbedding, ProjectedAdaptiveLogSoftmaxWithLoss
  from ..utils.config import CfgNode
  from ..sequence_sampler import BaseStepDecoder
label_desc: Use image node_id 1 to create a new TransformerXLDecoder object from inherited base classes: HybridBlock with example: obj = TransformerXLDecoder(num_layers, units, hidden_size, num_heads, activation_dropout, dropout, attention_dropout, layernorm_eps, activation, dtype, layout, pre_norm, weight_initializer, bias_initializer)