class_docstr: This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to
  instantiate a Decision Transformer 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 standard
  DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as
  part of the architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      state_dim (`int`, *optional*, defaults to 17):
          The state size for the RL environment
      act_dim (`int`, *optional*, defaults to 4):
          The size of the output action space
      hidden_size (`int`, *optional*, defaults to 128):
          The size of the hidden layers
      max_ep_len (`int`, *optional*, defaults to 4096):
          The maximum length of an episode in the environment
      action_tanh (`bool`, *optional*, defaults to True):
          Whether to use a tanh activation on action prediction
      vocab_size (`int`, *optional*, defaults to 50257):
          Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`DecisionTransformerModel`].
      n_positions (`int`, *optional*, defaults to 1024):
          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 768):
          Dimensionality of the embeddings and hidden states.
      n_layer (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      n_head (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      n_inner (`int`, *optional*):
          Dimensionality of the inner feed-forward layers. If unset, will default to 4 times `n_embd`.
      activation_function (`str`, *optional*, defaults to `"gelu"`):
          Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
      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-5):
          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.
      scale_attn_weights (`bool`, *optional*, defaults to `True`):
          Scale attention weights by dividing by sqrt(hidden_size)..
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
      scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
          Whether to additionally scale attention weights by `1 / layer_idx + 1`.
      reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
          Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
          dot-product/softmax to float() when training with mixed precision.
  
  Example:
  
  ```python
  >>> from transformers import DecisionTransformerConfig, DecisionTransformerModel
  
  >>> # Initializing a DecisionTransformer configuration
  >>> configuration = DecisionTransformerConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = DecisionTransformerModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      state_dim=17,
      act_dim=4,
      hidden_size=128,
      max_ep_len=4096,
      action_tanh=True,
      vocab_size=1,
      n_positions=1024,
      n_embd=768,
      n_layer=3,
      n_head=1,
      n_inner=None,
      activation_function="relu",
      resid_pdrop=0.1,
      embd_pdrop=0.1,
      attn_pdrop=0.1,
      layer_norm_epsilon=1e-5,
      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,
      scale_attn_weights=True,
      use_cache=True,
      bos_token_id=50256,
      eos_token_id=50256,
      scale_attn_by_inverse_layer_idx=False,
      reorder_and_upcast_attn=False,
      **kwargs,
  ):
      self.state_dim = state_dim
      self.act_dim = act_dim
      self.hidden_size = hidden_size
      self.max_ep_len = max_ep_len
      self.action_tanh = action_tanh
      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.n_inner = n_inner
      self.activation_function = activation_function
      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.scale_attn_weights = scale_attn_weights
      self.use_cache = use_cache
      self.scale_attn_by_inverse_layer_idx = (
          scale_attn_by_inverse_layer_idx
      )
      self.reorder_and_upcast_attn = reorder_and_upcast_attn
  
      self.bos_token_id = bos_token_id
      self.eos_token_id = eos_token_id
  
      super().__init__(
          bos_token_id=bos_token_id, 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 DecisionTransformerConfig object from inherited base classes: PretrainedConfig with example: obj = DecisionTransformerConfig(state_dim, act_dim, hidden_size, max_ep_len, action_tanh, vocab_size, n_positions, n_embd, n_layer, n_head, n_inner, activation_function, 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, scale_attn_weights, use_cache, bos_token_id, eos_token_id, scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn)