def __init__(
        self,
        num_embeddings,
        num_additional_embeddings,
        embedding_dim,
        partially_freeze: Optional[bool] = False,
        device=None,
        dtype=None,
        padding_idx=None,
        **kwargs,
    ) -> None:
        """
        Args:
            num_embeddings (`int`):
                Size of the dictionary of embeddings
            num_additional_embeddings (`int`):
                Number of additional embeddings. Only useful when you `partially_freeze=True`.
            embedding_dim (`int`):
                The size of each embedding vector
            partially_freeze: (`bool`, *optional*, defaults to `False`):
                If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
            padding_idx (`int`, *optional*):
                The padding index (needs to be less than num_embeddings)

        Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
        `max_norm` or `norm_type`. We are not supporting these.
        """
        if padding_idx is not None and padding_idx > num_embeddings:
            raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
        super().__init__(
            num_embeddings=num_embeddings,
            embedding_dim=embedding_dim,
            device=device,
            dtype=dtype,
            padding_idx=padding_idx,
            **kwargs,
        )
        self.num_embeddings = num_embeddings
        self.padding_idx = padding_idx
        self.num_additional_embeddings = num_additional_embeddings
        self.partially_freeze = partially_freeze

        if partially_freeze:
            self.weight.requires_grad_(False)

        if self.num_additional_embeddings > 0:
            self.additional_embedding = nn.Embedding(
                num_embeddings=self.num_additional_embeddings,
                embedding_dim=embedding_dim,
                device=device,
                dtype=dtype,
            )

        Args:
            num_embeddings (`int`):
                Size of the dictionary of embeddings
            num_additional_embeddings (`int`):
                Number of additional embeddings. Only useful when you `partially_freeze=True`.
            embedding_dim (`int`):
                The size of each embedding vector
            partially_freeze: (`bool`, *optional*, defaults to `False`):
                If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
            padding_idx (`int`, *optional*):
                The padding index (needs to be less than num_embeddings)

        Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
        `max_norm` or `norm_type`. We are not supporting these.
        
dpkd/transformers/src/transformers/models/idefics/modeling_idefics.py