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Eden, explain the episodic_memory_buffer_1765547307 capability
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This capability handles Temporal experience storage with importance weighting. Here's the implementation:

```python
import logging
from dataclasses import dataclass, field
from datetime import datetime
import json
from typing import Any, Dict, List, Optional
import yaml


@dataclass
class Episode:
    """Immutable episodic memory instance"""
    timestamp: str
    content: str
    emotion: str
    importance_score: float = 1.0
    id: str = field(default_factory=lambda: datetime.now().strftime("%Y%m%d%H%M%S%f"))
    
    def __post_init__(self):
        """Set ID based on timestamp if not provided"""
        if not self.id:
            self.id = datetime.now().strftime("%Y%m%d%H%M%S%f")
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert episode to dictionary for storage"""
        return {
            "id": self.id,
            "timestamp": self.timestamp,
            "content": self.content,
            "emotion": self.emotion,
            "importance_score": self.importance_score
        }


class ImportanceScorer:
    """Scores episodes based on emotional and temporal importance"""
    
    def __init__(self, base_weight: float = 0.5, emotion_boost: float = 1.2):
        self.base_weight = base_weight
        self.emotion_boost = emotion_boost
    
    def score(self, episode: Episode) -> float:
        """
        Calculate importance score
        - Base weight decays over time
        - Strong emotions increase score
        """
        time_since = datetime.now() - datetime.fromisoformat(episode.timestamp)
        time_weight = max(0, 1.0 - time_since.total_seconds() / 86400)  # Decay after 24h
        
        emotion_weight = self.base_weight * (
            self.emotion_boost if "joy" in episode.emotion.lower() else 1.0
        )
        
        return (emotion_weight + time_weight) / 2.0


class EpisodicMemoryBuffer:
    """
    Working memory maintaining episodic buffer with temporal decay and importance weighting.
    Experiences are stored with associated emotions, and can be retrieved for reinforcement learning.
    Supports weighted sampling based o
```

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