def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1):
        """
        A basic module that executes conv - norm - in sequence used in MaskFormer.

        Args:
            in_features (`int`):
                The number of input features (channels).
            out_features (`int`):
                The number of outputs features (channels).
        """
        super().__init__()
        self.layers = [
            nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=padding, bias=False),
            nn.GroupNorm(32, out_features),
            nn.ReLU(inplace=True),
        ]
        for i, layer in enumerate(self.layers):
            # Provide backwards compatibility from when the class inherited from nn.Sequential
            # In nn.Sequential subclasses, the name given to the layer is its index in the sequence.
            # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g.
            # self.my_layer_name = Layer()
            # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register
            # explicitly
            self.add_module(str(i), layer)

        A basic module that executes conv - norm - in sequence used in MaskFormer.

        Args:
            in_features (`int`):
                The number of input features (channels).
            out_features (`int`):
                The number of outputs features (channels).
        
dpkd/transformers/src/transformers/models/maskformer/modeling_maskformer.py