class_docstr: This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
  PEGASUS 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 PEGASUS
  [google/pegasus-large](https://huggingface.co/google/pegasus-large) 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 50265):
          Vocabulary size of the PEGASUS model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`PegasusModel`] or [`TFPegasusModel`].
      d_model (`int`, *optional*, defaults to 1024):
          Dimensionality of the layers and the pooler layer.
      encoder_layers (`int`, *optional*, defaults to 12):
          Number of encoder layers.
      decoder_layers (`int`, *optional*, defaults to 12):
          Number of decoder layers.
      encoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer decoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      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 0.0):
          The dropout ratio for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for activations inside the fully connected layer.
      classifier_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for classifier.
      max_position_embeddings (`int`, *optional*, defaults to 1024):
          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).
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      encoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      scale_embedding (`bool`, *optional*, defaults to `False`):
          Scale embeddings by diving by sqrt(d_model).
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models)
      forced_eos_token_id (`int`, *optional*, defaults to 1):
          The id of the token to force as the last generated token when `max_length` is reached. Usually set to
          `eos_token_id`.
  
  Example:
  
  ```python
  >>> from transformers import PegasusConfig, PegasusModel
  
  >>> # Initializing a PEGASUS google/pegasus-large style configuration
  >>> configuration = PegasusConfig()
  
  >>> # Initializing a model (with random weights) from the google/pegasus-large style configuration
  >>> model = PegasusModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def num_attention_heads(self) -> int:
      return self.encoder_attention_heads
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the PegasusConfig obj's underlying member method code with example usage: obj.num_attention_heads() and returns: self