class_docstr: This is the configuration class to store the configuration of a [`CTRLModel`] or a [`TFCTRLModel`]. It is used to
  instantiate a CTRL 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
  [ctrl](https://huggingface.co/ctrl) architecture from SalesForce.
  
  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 246534):
          Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`CTRLModel`] or [`TFCTRLModel`].
      n_positions (`int`, *optional*, defaults to 256):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      n_embd (`int`, *optional*, defaults to 1280):
          Dimensionality of the embeddings and hidden states.
      dff (`int`, *optional*, defaults to 8192):
          Dimensionality of the inner dimension of the feed forward networks (FFN).
      n_layer (`int`, *optional*, defaults to 48):
          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.
      resid_pdrop (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      embd_pdrop (`int`, *optional*, defaults to 0.1):
          The dropout ratio for the embeddings.
      attn_pdrop (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention.
      layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
          The epsilon to use in the layer normalization layers
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
  
  
  Examples:
  
  ```python
  >>> from transformers import CTRLConfig, CTRLModel
  
  >>> # Initializing a CTRL configuration
  >>> configuration = CTRLConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = CTRLModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=246534,
      n_positions=256,
      n_embd=1280,
      dff=8192,
      n_layer=48,
      n_head=16,
      resid_pdrop=0.1,
      embd_pdrop=0.1,
      attn_pdrop=0.1,
      layer_norm_epsilon=1e-6,
      initializer_range=0.02,
      summary_type="cls_index",
      summary_use_proj=True,
      summary_activation=None,
      summary_proj_to_labels=True,
      summary_first_dropout=0.1,
      use_cache=True,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.n_positions = n_positions
      self.n_embd = n_embd
      self.n_layer = n_layer
      self.n_head = n_head
      self.dff = dff
      self.resid_pdrop = resid_pdrop
      self.embd_pdrop = embd_pdrop
      self.attn_pdrop = attn_pdrop
      self.layer_norm_epsilon = layer_norm_epsilon
      self.initializer_range = initializer_range
  
      self.summary_type = summary_type
      self.summary_use_proj = summary_use_proj
      self.summary_activation = summary_activation
      self.summary_first_dropout = summary_first_dropout
      self.summary_proj_to_labels = summary_proj_to_labels
      self.use_cache = use_cache
  
      super().__init__(**kwargs)
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new CTRLConfig object from inherited base classes: PretrainedConfig with example: obj = CTRLConfig(vocab_size, n_positions, n_embd, dff, n_layer, n_head, resid_pdrop, embd_pdrop, attn_pdrop, layer_norm_epsilon, initializer_range, summary_type, summary_use_proj, summary_activation, summary_proj_to_labels, summary_first_dropout, use_cache)