class_docstr: This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
  used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture.
  Instantiating a configuration with the defaults will yield a similar configuration to that of the TransfoXL
  [transfo-xl-wt103](https://huggingface.co/transfo-xl-wt103) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 267735):
          Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`].
      cutoffs (`List[int]`, *optional*, defaults to `[20000, 40000, 200000]`):
          Cutoffs for the adaptive softmax.
      d_model (`int`, *optional*, defaults to 1024):
          Dimensionality of the model's hidden states.
      d_embed (`int`, *optional*, defaults to 1024):
          Dimensionality of the embeddings
      n_head (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      d_head (`int`, *optional*, defaults to 64):
          Dimensionality of the model's heads.
      d_inner (`int`, *optional*, defaults to 4096):
          Inner dimension in FF
      div_val (`int`, *optional*, defaults to 4):
          Divident value for adapative input and softmax
      pre_lnorm (`boolean`, *optional*, defaults to `False`):
          Whether or not to apply LayerNorm to the input instead of the output in the blocks.
      n_layer (`int`, *optional*, defaults to 18):
          Number of hidden layers in the Transformer encoder.
      mem_len (`int`, *optional*, defaults to 1600):
          Length of the retained previous heads.
      clamp_len (`int`, *optional*, defaults to 1000):
          Use the same pos embeddings after clamp_len.
      same_length (`boolean`, *optional*, defaults to `True`):
          Whether or not to use the same attn length for all tokens
      proj_share_all_but_first (`boolean`, *optional*, defaults to `True`):
          True to share all but first projs, False not to share.
      attn_type (`int`, *optional*, defaults to 0):
          Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
      sample_softmax (`int`, *optional*, defaults to -1):
          Number of samples in the sampled softmax.
      adaptive (`boolean`, *optional*, defaults to `True`):
          Whether or not to use adaptive softmax.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      dropatt (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      untie_r (`boolean`, *optional*, defaults to `True`):
          Whether ot not to untie relative position biases.
      init (`str`, *optional*, defaults to `"normal"`):
          Parameter initializer to use.
      init_range (`float`, *optional*, defaults to 0.01):
          Parameters initialized by U(-init_range, init_range).
      proj_init_std (`float`, *optional*, defaults to 0.01):
          Parameters initialized by N(0, init_std)
      init_std (`float`, *optional*, defaults to 0.02):
          Parameters initialized by N(0, init_std)
      layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
          The epsilon to use in the layer normalization layers
      eos_token_id (`int`, *optional*, defaults to 0):
          End of stream token id.
  
  Examples:
  
  ```python
  >>> from transformers import TransfoXLConfig, TransfoXLModel
  
  >>> # Initializing a Transformer XL configuration
  >>> configuration = TransfoXLConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = TransfoXLModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=267735,
      cutoffs=[20000, 40000, 200000],
      d_model=1024,
      d_embed=1024,
      n_head=16,
      d_head=64,
      d_inner=4096,
      div_val=4,
      pre_lnorm=False,
      n_layer=18,
      mem_len=1600,
      clamp_len=1000,
      same_length=True,
      proj_share_all_but_first=True,
      attn_type=0,
      sample_softmax=-1,
      adaptive=True,
      dropout=0.1,
      dropatt=0.0,
      untie_r=True,
      init="normal",
      init_range=0.01,
      proj_init_std=0.01,
      init_std=0.02,
      layer_norm_epsilon=1e-5,
      eos_token_id=0,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.cutoffs = []
      self.cutoffs.extend(cutoffs)
      if proj_share_all_but_first:
          self.tie_projs = [False] + [True] * len(self.cutoffs)
      else:
          self.tie_projs = [False] + [False] * len(self.cutoffs)
      self.d_model = d_model
      self.d_embed = d_embed
      self.d_head = d_head
      self.d_inner = d_inner
      self.div_val = div_val
      self.pre_lnorm = pre_lnorm
      self.n_layer = n_layer
      self.n_head = n_head
      self.mem_len = mem_len
      self.same_length = same_length
      self.attn_type = attn_type
      self.clamp_len = clamp_len
      self.sample_softmax = sample_softmax
      self.adaptive = adaptive
      self.dropout = dropout
      self.dropatt = dropatt
      self.untie_r = untie_r
      self.init = init
      self.init_range = init_range
      self.proj_init_std = proj_init_std
      self.init_std = init_std
      self.layer_norm_epsilon = layer_norm_epsilon
      super().__init__(eos_token_id=eos_token_id, **kwargs)
  
imports: from ....configuration_utils import PretrainedConfig
  from ....utils import logging
label_desc: Use image node_id 1 to create a new TransfoXLConfig object from inherited base classes: PretrainedConfig with example: obj = TransfoXLConfig(vocab_size, cutoffs, d_model, d_embed, n_head, d_head, d_inner, div_val, pre_lnorm, n_layer, mem_len, clamp_len, same_length, proj_share_all_but_first, attn_type, sample_softmax, adaptive, dropout, dropatt, untie_r, init, init_range, proj_init_std, init_std, layer_norm_epsilon, eos_token_id)