code: def __init__(
      self,
      attention_head_size=64,
      attn_layers=["local", "lsh", "local", "lsh", "local", "lsh"],
      axial_norm_std=1.0,
      axial_pos_embds=True,
      axial_pos_shape=[64, 64],
      axial_pos_embds_dim=[64, 192],
      chunk_size_lm_head=0,
      eos_token_id=2,
      feed_forward_size=512,
      hash_seed=None,
      hidden_act="relu",
      hidden_dropout_prob=0.05,
      hidden_size=256,
      initializer_range=0.02,
      is_decoder=False,
      layer_norm_eps=1e-12,
      local_num_chunks_before=1,
      local_num_chunks_after=0,
      local_attention_probs_dropout_prob=0.05,
      local_attn_chunk_length=64,
      lsh_attn_chunk_length=64,
      lsh_attention_probs_dropout_prob=0.0,
      lsh_num_chunks_before=1,
      lsh_num_chunks_after=0,
      max_position_embeddings=4096,
      num_attention_heads=12,
      num_buckets=None,
      num_hashes=1,
      pad_token_id=0,
      vocab_size=320,
      tie_word_embeddings=False,
      use_cache=True,
      classifier_dropout=None,
      **kwargs,
  ):
      self.hash_seed = hash_seed
      self.vocab_size = vocab_size
      self.attention_head_size = attention_head_size
      self.hidden_size = hidden_size
      self.num_attention_heads = num_attention_heads
      self.num_hashes = num_hashes
      self.num_hidden_layers = len(attn_layers)
      self.num_buckets = (
          tuple(num_buckets)
          if isinstance(num_buckets, list)
          else num_buckets
      )
      self.lsh_attn_chunk_length = lsh_attn_chunk_length
      self.local_attn_chunk_length = local_attn_chunk_length
      self.lsh_num_chunks_after = lsh_num_chunks_after
      self.lsh_num_chunks_before = lsh_num_chunks_before
      self.local_num_chunks_after = local_num_chunks_after
      self.local_num_chunks_before = local_num_chunks_before
      self.hidden_act = hidden_act
      self.feed_forward_size = feed_forward_size
      self.hidden_dropout_prob = hidden_dropout_prob
      self.lsh_attention_probs_dropout_prob = (
          lsh_attention_probs_dropout_prob
      )
      self.local_attention_probs_dropout_prob = (
          local_attention_probs_dropout_prob
      )
      self.max_position_embeddings = max_position_embeddings
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.axial_pos_embds = axial_pos_embds
      self.axial_pos_shape = tuple(axial_pos_shape)
      self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
      self.axial_norm_std = axial_norm_std
      self.chunk_size_lm_head = chunk_size_lm_head
      self.attn_layers = attn_layers
      self.use_cache = use_cache
      self.classifier_dropout = classifier_dropout
      super().__init__(
          pad_token_id=pad_token_id,
          eos_token_id=eos_token_id,
          is_decoder=is_decoder,
          tie_word_embeddings=tie_word_embeddings,
          **kwargs,
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new ReformerConfig object from inherited base classes: PretrainedConfig with example: obj = ReformerConfig(attention_head_size, attn_layers, axial_norm_std, axial_pos_embds, axial_pos_shape, axial_pos_embds_dim, chunk_size_lm_head, eos_token_id, feed_forward_size, hash_seed, hidden_act, hidden_dropout_prob, hidden_size, initializer_range, is_decoder, layer_norm_eps, local_num_chunks_before, local_num_chunks_after, local_attention_probs_dropout_prob, local_attn_chunk_length, lsh_attn_chunk_length, lsh_attention_probs_dropout_prob, lsh_num_chunks_before, lsh_num_chunks_after, max_position_embeddings, num_attention_heads, num_buckets, num_hashes, pad_token_id, vocab_size, tie_word_embeddings, use_cache, classifier_dropout)