code: def __init__(
      self,
      units: int = 512,
      hidden_size: int = 2048,
      num_heads: int = 8,
      activation_dropout: float = 0.0,
      dropout: float = 0.1,
      attention_dropout: float = 0.1,
      layer_norm_eps: float = 1e-5,
      activation: str = "relu",
      weight_initializer=None,
      bias_initializer="zeros",
      pre_norm=False,
      dtype="float32",
      layout="NT",
  ):
      super().__init__()
      self._pre_norm = pre_norm
      self._dtype = dtype
      self._num_heads = num_heads
      self._layout = layout
      assert layout in ["NT", "TN"], "Unknown layout = {}".format(
          layout
      )
      if layout == "NT":
          self._cell_layout = "NTK"
      elif layout == "TN":
          self._cell_layout = "TNK"
      else:
          raise NotImplementedError
      assert units % num_heads == 0
      self.dropout_layer = nn.Dropout(dropout)
      self.attn_query = nn.Dense(
          units,
          in_units=units,
          use_bias=False,
          flatten=False,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          dtype=dtype,
      )
      self.attn_kv = nn.Dense(
          2 * units,
          in_units=units,
          use_bias=False,
          flatten=False,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          dtype=dtype,
      )
      self.rel_pos_score_cell = RelAttentionScoreCell(
          query_units=units,
          num_heads=num_heads,
          bidirectional=False,
          method="transformer_xl",
          dropout=dropout,
          dtype=dtype,
          layout=self._cell_layout,
      )
      self.attn_cell = MultiHeadAttentionCell(
          query_units=units,
          num_heads=num_heads,
          attention_dropout=attention_dropout,
          dtype=dtype,
          layout=self._cell_layout,
      )
      self.out_proj = nn.Dense(
          units,
          in_units=units,
          use_bias=False,
          flatten=False,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          dtype=dtype,
      )
      self.layer_norm = nn.LayerNorm(
          epsilon=layer_norm_eps, in_channels=units
      )
      self.ffn = PositionwiseFFN(
          units=units,
          hidden_size=hidden_size,
          activation=activation,
          activation_dropout=activation_dropout,
          dropout=dropout,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          layer_norm_eps=layer_norm_eps,
          pre_norm=pre_norm,
          dtype=dtype,
      )
  
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 TransformerXLDecoderLayer object from inherited base classes: HybridBlock with example: obj = TransformerXLDecoderLayer(units, hidden_size, num_heads, activation_dropout, dropout, attention_dropout, layer_norm_eps, activation, weight_initializer, bias_initializer, pre_norm, dtype, layout)