def __init__(
        self,
        input_shapes,
        input_names: Optional[list] = None,
        input_types: Optional[list] = None,
        max_samples: Optional[int] = 32,
        **kwargs,
    ):
        """Initialize the dataset with dummy data.

        if input_names is not provided, the dataset will return a tuple of tensors
        else the dataset will return a dict of tensors
        """
        # pylint: disable=super-init-not-called
        if not input_types:
            input_types = ["float32"] * len(input_shapes)
        input_types = [resolve_torch_dtype(dtype_str) for dtype_str in input_types]

        if input_names:
            dummy_data = {}
            for input_name, input_shape, input_type in zip(input_names, input_shapes, input_types):
                dummy_data.update({input_name: torch.ones(input_shape, dtype=input_type)})
            dummy_data = dummy_data if len(dummy_data) > 1 else dummy_data[input_names[0]]
        else:
            dummy_data = []
            for shape, dtype in zip(input_shapes, input_types):
                dummy_data.append(torch.ones(shape, dtype=dtype))
            dummy_data = tuple(dummy_data) if len(dummy_data) > 1 else dummy_data[0]

        self.max_samples = max_samples
        self.dummy_data = dummy_data, torch.tensor([0])
Initialize the dataset with dummy data.

        if input_names is not provided, the dataset will return a tuple of tensors
        else the dataset will return a dict of tensors
        
olive/data/component/dataset.py