code: def __init__(self, cfg=None):
      super().__init__()
      if cfg is None:
          cfg = TransformerXLForLM.get_cfg()
      else:
          cfg = TransformerXLForLM.get_cfg().clone_merge(cfg)
      self._cfg = cfg
      assert cfg.MODEL.vocab_size > 0
      weight_initializer = mx.init.create(*cfg.INITIALIZER.weight)
      embed_initializer = mx.init.create(*cfg.INITIALIZER.embed)
      bias_initializer = mx.init.create(*cfg.INITIALIZER.bias)
      self._num_layers = cfg.MODEL.num_layers
      self._layout = cfg.MODEL.layout
      self._units = cfg.MODEL.units
      self._dtype = cfg.MODEL.dtype
      assert cfg.MODEL.units % cfg.MODEL.num_heads == 0
      self.word_emb = AdaptiveEmbedding(
          vocab_size=cfg.MODEL.vocab_size,
          embed_size=cfg.MODEL.embed_units,
          units=cfg.MODEL.units,
          cutoffs=cfg.MODEL.cutoffs,
          div_val=cfg.MODEL.div_val,
          scaled=True,
          embedding_initializer=embed_initializer,
          weight_initializer=weight_initializer,
          dtype=cfg.MODEL.dtype,
      )
      self.dropout_layer = nn.Dropout(cfg.MODEL.dropout)
      self.decoder = TransformerXLDecoder(
          num_layers=cfg.MODEL.num_layers,
          units=cfg.MODEL.units,
          hidden_size=cfg.MODEL.hidden_size,
          num_heads=cfg.MODEL.num_heads,
          activation_dropout=cfg.MODEL.activation_dropout,
          dropout=cfg.MODEL.dropout,
          attention_dropout=cfg.MODEL.attention_dropout,
          layernorm_eps=cfg.MODEL.layernorm_eps,
          activation=cfg.MODEL.activation,
          dtype=cfg.MODEL.dtype,
          layout=cfg.MODEL.layout,
          pre_norm=cfg.MODEL.pre_norm,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
      )
      if cfg.MODEL.tie_weights and cfg.MODEL.tie_projs:
          crit_params = self.word_emb.collect_params(
              "(embed|inter_proj)"
          )
      elif cfg.MODEL.tie_weights and not cfg.MODEL.tie_projs:
          crit_params = self.word_emb.collect_params("embed")
      elif not cfg.MODEL.tie_weights and cfg.MODEL.tie_projs:
          crit_params = self.word_emb.collect_params("inter_proj")
      else:
          crit_params = None
      self.crit = ProjectedAdaptiveLogSoftmaxWithLoss(
          vocab_size=cfg.MODEL.vocab_size,
          embed_size=cfg.MODEL.embed_units,
          in_units=cfg.MODEL.units,
          cutoffs=cfg.MODEL.cutoffs,
          div_val=cfg.MODEL.div_val,
          dtype=cfg.MODEL.dtype,
          use_bias=True,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
      )
      self.crit.share_parameters(crit_params)
  
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 TransformerXLForLM object from inherited base classes: Block with example: obj = TransformerXLForLM(cfg)