ses bias in the attention module - unsupported at the moment.
        embed_scale(`float`, *optional*, defaults to None):
            Scaling factor for the node embeddings.
        num_trans_layers_to_freeze (`int`, *optional*, defaults to 0):
            Number of transformer layers to freeze.
        encoder_normalize_before (`bool`, *optional*, defaults to `False`):
            Normalize features before encoding the graph.
        pre_layernorm (`bool`, *optional*, defaults to `False`):
            Apply layernorm before self attention and the feed forward network. Without this, post layernorm will be
            used.
        apply_graphormer_init (`bool`, *optional*, defaults to `False`):
            Apply a custom graphormer initialisation to the model before training.
        freeze_embeddings (`bool`, *optional*, defaults to `False`):
            Freeze the embedding layer, or train it along the model.
        encoder_normalize_before (`bool`, *optional*, defaults to `False`):
            Apply the layer norm before each encoder block.
        q_noise (`float`, *optional*, defaults to 0.0):
            Amount of quantization noise (see "Training with Quantization Noise for Extreme Model Compression"). (For
            more detail, see fairseq's documentation on quant_noise).
        qn_block_size (`int`, *optional*, defaults to 8):
            Size of the blocks for subsequent quantization with iPQ (see q_noise).
        kdim (`int`, *optional*, defaults to None):
            Dimension of the key in the attention, if different from the other values.
        vdim (`int`, *optional*, defaults to None):
            Dimension of the value in the attention, if different from the other values.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        traceable (`bool`, *optional*, defaults to `False`):
            Changes return value of the encoder's inner_state to stacked tensors.

        Example:
            ```python
            >>> from transformers import GraphormerForGraphClassification, GraphormerConfig

            >>> # Initializing a Graphormer graphormer-base-pcqm4mv2 style configuration
            >>> configuration = GraphormerConfig()

            >>> # Initializing a model from the graphormer-base-pcqm4mv1 style configuration
            >>> model = GraphormerForGraphClassification(configuration)

            >>> # Accessing the model configuration
            >>> configuration = model.config
            ```
    Z
graphormerZpast_key_valuesé