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#!/usr/bin/env pybricks-micropython

"""
Example LEGO® MINDSTORMS® EV3 Robot Educator Driving Base Program
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This program requires LEGO® EV3 MicroPython v2.0.
Download: https://education.lego.com/en-us/support/mindstorms-ev3/python-for-ev3

Building instructions can be found at:

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from fastapi import FastAPI, BackgroundTasks, Request
import uvicorn
import requests
import asyncio
import logging
import sys

from models import *
from blockchain.db import DB
from blockchain.blockchain import Blockchain

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from .blocks import Block, Tx, Input, Output
from .verifiers import TxVerifier, BlockOutOfChain, BlockVerifier, BlockVerificationFailed
import logging

logger = logging.getLogger('Blockchain')


class Blockchain: 

    __slots__ =  'max_nonce', 'chain', 'unconfirmed_transactions', 'db', 'wallet', 'on_new_block', 'on_prev_block', 'current_block_transactions', 'fork_blocks'

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import rsa
import binascii

from .wallet import Address

class TxVerifier:
    def __init__(self, db):
        self.db = db

    def verify(self, inputs, outputs):

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import time
from hashlib import sha256
from merkletools import MerkleTools

from .wallet import Address


class Input:
    __slots__ = 'prev_tx_hash', 'output_index', 'signature', '_hash', 'address', 'index', 'amount'

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import rsa
import binascii


class Address:
    def __init__(self, addr):
        if isinstance(addr, rsa.PublicKey):
            self.addr = addr
        else:
            if isinstance(addr,str):

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from sklearn.datasets import make_blobs
from collections import defaultdict

# Generate sample data
n_samples = 4000
n_components = 4

X, y_true = make_blobs(n_samples=n_samples,
                       centers=n_components,
                       cluster_std=0.99,

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import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

# Load the diabetes dataset
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)

# Use only one feature
diabetes_X = diabetes_X

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import torch
import torch.nn as nn
import torch.nn.functional as F


class Conv2d(nn.Module):

    def __init__(self, inch, outch, kernel, padding=0, stride=1):
        super(Conv2d, self).__init__()
        # setting our kernels with random normal

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