def __init__(
        self,
        num_patches: int,
        depth: int,
        mlp_dim: int,
        predictor_embed_dim: int,
        num_heads: int,
        qkv_bias: bool = True,
        mlp_ratio: float = 4.0,
        drop_path_rate: float = 0.0,
        proj_drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        norm_layer: Callable[..., nn.Module] = partial(nn.LayerNorm, eps=1e-6),
    ):
        """Initializes the IJEPAPredictorTIMM with the specified dimensions."""

        super().__init__()

        self.predictor_embed = nn.Linear(mlp_dim, predictor_embed_dim, bias=True)
        self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
        self.predictor_proj = nn.Linear(predictor_embed_dim, mlp_dim, bias=True)
        self.predictor_norm = norm_layer(predictor_embed_dim)
        self.predictor_pos_embed = nn.Parameter(
            torch.zeros(1, num_patches, predictor_embed_dim), requires_grad=False
        )
        predictor_pos_embed = utils.get_2d_sincos_pos_embed(
            self.predictor_pos_embed.shape[-1], int(num_patches**0.5), cls_token=False
        )
        self.predictor_pos_embed.data.copy_(
            torch.from_numpy(predictor_pos_embed).float().unsqueeze(0)
        )

        # original implementation also has drop path rate
        self.predictor_blocks = nn.ModuleList(
            [
                Block(
                    dim=predictor_embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop_path=drop_path_rate,
                    proj_drop=proj_drop_rate,
                    attn_drop=attn_drop_rate,
                    norm_layer=norm_layer,
                )
                for _ in range(depth)
            ]
        )
Initializes the IJEPAPredictorTIMM with the specified dimensions.
lightly/models/modules/ijepa_timm.py