class_docstr: This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to
  instantiate a XLNet 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
  [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) 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 32000):
          Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`XLNetModel`] or [`TFXLNetModel`].
      d_model (`int`, *optional*, defaults to 1024):
          Dimensionality of the encoder layers and the pooler layer.
      n_layer (`int`, *optional*, defaults to 24):
          Number of hidden layers in the Transformer encoder.
      n_head (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      d_inner (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and
          `"gelu_new"` are supported.
      untie_r (`bool`, *optional*, defaults to `True`):
          Whether or not to untie relative position biases
      attn_type (`str`, *optional*, defaults to `"bi"`):
          The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      mem_len (`int` or `None`, *optional*):
          The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous
          forward pass won't be re-computed. See the
          [quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information.
      reuse_len (`int`, *optional*):
          The number of tokens in the current batch to be cached and reused in the future.
      bi_data (`bool`, *optional*, defaults to `False`):
          Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False`
          during finetuning.
      clamp_len (`int`, *optional*, defaults to -1):
          Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping.
      same_length (`bool`, *optional*, defaults to `False`):
          Whether or not to use the same attention length for each token.
      summary_type (`str`, *optional*, defaults to "last"):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Has to be one of the following options:
  
              - `"last"`: Take the last token hidden state (like XLNet).
              - `"first"`: Take the first token hidden state (like BERT).
              - `"mean"`: Take the mean of all tokens hidden states.
              - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
              - `"attn"`: Not implemented now, use multi-head attention.
      summary_use_proj (`bool`, *optional*, defaults to `True`):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Whether or not to add a projection after the vector extraction.
      summary_activation (`str`, *optional*):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
      summary_proj_to_labels (`boo`, *optional*, defaults to `True`):
          Used in the sequence classification and multiple choice models.
  
          Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
      summary_last_dropout (`float`, *optional*, defaults to 0.1):
          Used in the sequence classification and multiple choice models.
  
          The dropout ratio to be used after the projection and activation.
      start_n_top (`int`, *optional*, defaults to 5):
          Used in the SQuAD evaluation script.
      end_n_top (`int`, *optional*, defaults to 5):
          Used in the SQuAD evaluation script.
      use_mems_eval (`bool`, *optional*, defaults to `True`):
          Whether or not the model should make use of the recurrent memory mechanism in evaluation mode.
      use_mems_train (`bool`, *optional*, defaults to `False`):
          Whether or not the model should make use of the recurrent memory mechanism in train mode.
  
          <Tip>
  
          For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to
          set `use_mems_train` to `False` as discussed
          [here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to
          `True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This
          line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of
          equal size.
  
          </Tip>
  
  Examples:
  
  ```python
  >>> from transformers import XLNetConfig, XLNetModel
  
  >>> # Initializing a XLNet configuration
  >>> configuration = XLNetConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = XLNetModel(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: import warnings
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 3 for calling the XLNetConfig obj's underlying member method code with example usage: obj.max_position_embeddings(value) without return types