typing
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@ -9,7 +9,7 @@ import tqdm
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def load_previous_and_future_frames(
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def load_previous_and_future_frames(
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item: dict[str, torch.Tensor],
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item: dict[str, torch.Tensor],
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hf_dataset: dict[str, datasets.Dataset],
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hf_dataset: datasets.Dataset,
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delta_timestamps: dict[str, list[float]],
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delta_timestamps: dict[str, list[float]],
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tol: float = 0.04,
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tol: float = 0.04,
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) -> dict[torch.Tensor]:
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) -> dict[torch.Tensor]:
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@ -25,7 +25,7 @@ def load_previous_and_future_frames(
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Parameters:
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Parameters:
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- item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
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- item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
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- hf_dataset (dict): A dictionary containing the full dataset. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
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- hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
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- delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be retrieved. These deltas are added to the item timestamp to form the query timestamps.
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- delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be retrieved. These deltas are added to the item timestamp to form the query timestamps.
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- tol (float, optional): The tolerance level used to determine if a data point is close enough to the query timestamp. Defaults to 0.04.
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- tol (float, optional): The tolerance level used to determine if a data point is close enough to the query timestamp. Defaults to 0.04.
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