class_docstr: This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
  instantiate a Funnel 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 Funnel
  Transformer [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) 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 30522):
          Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented
          by the `inputs_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`].
      block_sizes (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
          The sizes of the blocks used in the model.
      block_repeats (`List[int]`, *optional*):
          If passed along, each layer of each block is repeated the number of times indicated.
      num_decoder_layers (`int`, *optional*, defaults to 2):
          The number of layers in the decoder (when not using the base model).
      d_model (`int`, *optional*, defaults to 768):
          Dimensionality of the model's hidden states.
      n_head (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      d_head (`int`, *optional*, defaults to 64):
          Dimensionality of the model's heads.
      d_inner (`int`, *optional*, defaults to 3072):
          Inner dimension in the feed-forward blocks.
      hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      hidden_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability used between the two layers of the feed-forward blocks.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          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).
      type_vocab_size (`int`, *optional*, defaults to 3):
          The vocabulary size of the `token_type_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`].
      initializer_range (`float`, *optional*, defaults to 0.1):
          The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers.
      initializer_std (`float`, *optional*):
          The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of
          linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for
          linear layers.
      layer_norm_eps (`float`, *optional*, defaults to 1e-9):
          The epsilon used by the layer normalization layers.
      pooling_type (`str`, *optional*, defaults to `"mean"`):
          Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block.
      attention_type (`str`, *optional*, defaults to `"relative_shift"`):
          Possible values are `"relative_shift"` or `"factorized"`. The former is faster on CPU/GPU while the latter
          is faster on TPU.
      separate_cls (`bool`, *optional*, defaults to `True`):
          Whether or not to separate the cls token when applying pooling.
      truncate_seq (`bool`, *optional*, defaults to `False`):
          When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a
          sequence length that is not a multiple of 2.
      pool_q_only (`bool`, *optional*, defaults to `False`):
          Whether or not to apply the pooling only to the query or to query, key and values for the attention layers.
code: def num_hidden_layers(self):
      return sum(self.block_sizes)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/funnel/configuration_funnel.py
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the FunnelConfig obj's underlying member method code with example usage: obj.num_hidden_layers() and returns: sum