code: def get_cfg(cls, key=None):
      if key is None:
          config = CN()
          config.MODEL = CN()
          # For embedding
          config.MODEL.vocab_size = 1000
          config.MODEL.cutoffs = []
          config.MODEL.div_val = 1.0
          config.MODEL.embed_units = 512
          config.MODEL.tie_weights = True
          config.MODEL.tie_projs = True
          # For decoder
          config.MODEL.units = 512
          config.MODEL.hidden_size = 2048
          config.MODEL.num_layers = 6
          config.MODEL.num_heads = 8
          config.MODEL.activation = "relu"
          config.MODEL.pre_norm = False
          config.MODEL.dropout = 0.1
          config.MODEL.activation_dropout = 0.1
          config.MODEL.attention_dropout = 0.0
          config.MODEL.layernorm_eps = 1e-6
          config.MODEL.dtype = "float32"
          config.MODEL.layout = "TN"
          # For memory
          config.MODEL.mem_length = 512
          # Initialization
          config.INITIALIZER = CN()
          config.INITIALIZER.weight = ["normal", 0.02]
          config.INITIALIZER.bias = ["zeros"]
          config.INITIALIZER.embed = ["normal", 0.02]
      else:
          raise NotImplementedError
      return config
  
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 4 for calling the TransformerXLForLM obj's underlying member method code with example usage: obj.get_cfg(cls, key) and returns: config