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from __future__ import annotations
from dataclasses import dataclass
import torch
@dataclass
class SegmentId:
episode_id: int
start: int
stop: int
@dataclass
class Segment:
observations: torch.ByteTensor
actions: torch.LongTensor
rewards: torch.FloatTensor
ends: torch.LongTensor
mask_padding: torch.BoolTensor
id: SegmentId
@property
def effective_size(self) -> int:
return self.mask_padding.sum().item()
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