olute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        cross_attention_frequency (`int`, *optional*, defaults to 2):
            The frequency of adding cross-attention to the Transformer layers.
        encoder_hidden_size (`int`, *optional*, defaults to 1408):
            The hidden size of the hidden states for cross-attention.

    Examples:

    ```python
    >>> from transformers import InstructBlipQFormerConfig, InstructBlipQFormerModel

    >>> # Initializing a InstructBLIP Salesforce/instruct-blip-flan-t5 style configuration
    >>> configuration = InstructBlipQFormerConfig()

    >>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
    >>> model = InstructBlipQFormerModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Z