code: def __init__(
      self,
      vocab_size=36000,
      max_position_embeddings=1280,
      d_model=1024,
      d_ff=8192,
      d_ext=4096,
      d_spout=128,
      num_switch_layers=10,
      num_ext_layers=0,
      num_heads=16,
      num_experts=16,
      expert_capacity=128,
      dropout_rate=0.0,
      layer_norm_epsilon=1e-5,
      router_bias=False,
      router_jitter_noise=0.0,
      router_dtype="float32",
      router_ignore_padding_tokens=False,
      output_hidden_states=False,
      output_attentions=False,
      initializer_factor=0.002,
      output_router_logits=False,
      use_cache=True,
      separator_token_id=35998,
      pad_token_id=35995,
      eos_token_id=35999,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.d_model = d_model
      self.d_ff = d_ff
      self.d_ext = d_ext
      self.d_spout = d_spout
      self.num_switch_layers = num_switch_layers
      self.num_ext_layers = num_ext_layers
      self.num_layers = num_switch_layers + num_ext_layers
      self.num_heads = num_heads
      self.num_experts = num_experts
      self.expert_capacity = expert_capacity
      self.dropout_rate = dropout_rate
      self.layer_norm_epsilon = layer_norm_epsilon
      self.router_bias = router_bias
      self.router_jitter_noise = router_jitter_noise
      self.router_dtype = router_dtype
      self.router_ignore_padding_tokens = router_ignore_padding_tokens
      self.output_hidden_states = output_hidden_states
      self.output_attentions = output_attentions
      self.initializer_factor = initializer_factor
      self.output_router_logits = output_router_logits
      self.use_cache = use_cache
  
      super().__init__(
          separator_token_id=separator_token_id,
          pad_token_id=pad_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 GPTSanJapaneseConfig object from inherited base classes: PretrainedConfig with example: obj = GPTSanJapaneseConfig(vocab_size, max_position_embeddings, d_model, d_ff, d_ext, d_spout, num_switch_layers, num_ext_layers, num_heads, num_experts, expert_capacity, dropout_rate, layer_norm_epsilon, router_bias, router_jitter_noise, router_dtype, router_ignore_padding_tokens, output_hidden_states, output_attentions, initializer_factor, output_router_logits, use_cache, separator_token_id, pad_token_id, eos_token_id)