lerobot/common/policies/diffusion/configuration_diffusion.py [210:232]:
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        if self.crop_shape is not None:
            for key, image_ft in self.image_features.items():
                if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                    raise ValueError(
                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
                        f"for `crop_shape` and {image_ft.shape} for "
                        f"`{key}`."
                    )

        # Check that all input images have the same shape.
        first_image_key, first_image_ft = next(iter(self.image_features.items()))
        for key, image_ft in self.image_features.items():
            if image_ft.shape != first_image_ft.shape:
                raise ValueError(
                    f"`{key}` does not match `{first_image_key}`, but we expect all image shapes to match."
                )

    @property
    def observation_delta_indices(self) -> list:
        return list(range(1 - self.n_obs_steps, 1))

    @property
    def action_delta_indices(self) -> list:
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lerobot/common/policies/vqbet/configuration_vqbet.py [173:195]:
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        if self.crop_shape is not None:
            for key, image_ft in self.image_features.items():
                if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                    raise ValueError(
                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
                        f"for `crop_shape` and {image_ft.shape} for "
                        f"`{key}`."
                    )

        # Check that all input images have the same shape.
        first_image_key, first_image_ft = next(iter(self.image_features.items()))
        for key, image_ft in self.image_features.items():
            if image_ft.shape != first_image_ft.shape:
                raise ValueError(
                    f"`{key}` does not match `{first_image_key}`, but we expect all image shapes to match."
                )

    @property
    def observation_delta_indices(self) -> list:
        return list(range(1 - self.n_obs_steps, 1))

    @property
    def action_delta_indices(self) -> list:
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