class_docstr: This is the configuration class to store the configuration of a [`MT5Model`] or a [`TFMT5Model`]. It is used to
  instantiate a mT5 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 mT5
  [google/mt5-small](https://huggingface.co/google/mt5-small) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Arguments:
      vocab_size (`int`, *optional*, defaults to 250112):
          Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
      d_model (`int`, *optional*, defaults to 512):
          Size of the encoder layers and the pooler layer.
      d_kv (`int`, *optional*, defaults to 64):
          Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
          num_heads`.
      d_ff (`int`, *optional*, defaults to 1024):
          Size of the intermediate feed forward layer in each `T5Block`.
      num_layers (`int`, *optional*, defaults to 8):
          Number of hidden layers in the Transformer encoder.
      num_decoder_layers (`int`, *optional*):
          Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
      num_heads (`int`, *optional*, defaults to 6):
          Number of attention heads for each attention layer in the Transformer encoder.
      relative_attention_num_buckets (`int`, *optional*, defaults to 32):
          The number of buckets to use for each attention layer.
      relative_attention_max_distance (`int`, *optional*, defaults to 128):
          The maximum distance of the longer sequences for the bucket separation.
      dropout_rate (`float`, *optional*, defaults to 0.1):
          The ratio for all dropout layers.
      layer_norm_eps (`float`, *optional*, defaults to 1e-6):
          The epsilon used by the layer normalization layers.
      initializer_factor (`float`, *optional*, defaults to 1):
          A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
          testing).
      feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`):
          Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
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code: def num_hidden_layers(self):
      return self.num_layers
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/mt5/configuration_mt5.py
imports: from typing import Mapping
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeq2SeqConfigWithPast
  from ...utils import logging
label_desc: Use image node_id 4 for calling the MT5Config obj's underlying member method code with example usage: obj.num_hidden_layers() and returns: self