def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
        """
        A classic Multi Layer Perceptron (MLP).

        Args:
            input_dim (`int`):
                The input dimensions.
            hidden_dim (`int`):
                The hidden dimensions.
            output_dim (`int`):
                The output dimensions.
            num_layers (int, *optional*, defaults to 3):
                The number of layers.
        """
        super().__init__()
        in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
        out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]

        layers = []
        for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):
            layers.append(
                PredictionBlock(in_dim, out_dim, activation=nn.ReLU() if i < num_layers - 1 else nn.Identity())
            )

        self.layers = nn.Sequential(*layers)

        A classic Multi Layer Perceptron (MLP).

        Args:
            input_dim (`int`):
                The input dimensions.
            hidden_dim (`int`):
                The hidden dimensions.
            output_dim (`int`):
                The output dimensions.
            num_layers (int, *optional*, defaults to 3):
                The number of layers.
        
dpkd/transformers/src/transformers/models/oneformer/modeling_oneformer.py