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):
          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-5):
          The epsilon to use in the layer normalization layers
  
  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 max_position_embeddings(self, value):
      # Message copied from Transformer-XL documentation
      raise NotImplementedError(
          f"The model {self.model_type} is one of the few models that has no sequence length limit."
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 3 for calling the TransfoXLConfig obj's underlying member method code with example usage: obj.max_position_embeddings(value) without return types