class_docstr: This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a
  CodeGen 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 CodeGen
  [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) 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 50400):
          Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`CodeGenModel`].
      n_positions (`int`, *optional*, defaults to 2048):
          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 4096):
          Dimensionality of the embeddings and hidden states.
      n_layer (`int`, *optional*, defaults to 28):
          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.
      rotary_dim (`int`, *optional*, defaults to 64):
          Number of dimensions in the embedding that Rotary Position Embedding is applied to.
      n_inner (`int`, *optional*, defaults to None):
          Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
      activation_function (`str`, *optional*, defaults to `"gelu_new"`):
          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).
  
  Example:
  
  ```python
  >>> from transformers import CodeGenConfig, CodeGenModel
  
  >>> # Initializing a CodeGen 6B configuration
  >>> configuration = CodeGenConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = CodeGenModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=50400,
      n_positions=2048,
      n_ctx=2048,
      n_embd=4096,
      n_layer=28,
      n_head=16,
      rotary_dim=64,
      n_inner=None,
      activation_function="gelu_new",
      resid_pdrop=0.0,
      embd_pdrop=0.0,
      attn_pdrop=0.0,
      layer_norm_epsilon=1e-5,
      initializer_range=0.02,
      scale_attn_weights=True,
      use_cache=True,
      bos_token_id=50256,
      eos_token_id=50256,
      tie_word_embeddings=False,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.n_ctx = n_ctx
      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.rotary_dim = rotary_dim
      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.scale_attn_weights = scale_attn_weights
      self.use_cache = use_cache
  
      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,
          tie_word_embeddings=tie_word_embeddings,
          **kwargs,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/codegen/configuration_codegen.py
imports: from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  from ...None import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
label_desc: Use image node_id 1 to create a new CodeGenConfig object from inherited base classes: PretrainedConfig with example: obj = CodeGenConfig(vocab_size, n_positions, n_ctx, n_embd, n_layer, n_head, rotary_dim, n_inner, activation_function, resid_pdrop, embd_pdrop, attn_pdrop, layer_norm_epsilon, initializer_range, scale_attn_weights, use_cache, bos_token_id, eos_token_id, tie_word_embeddings)