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global DATA;
DATA = [];

for (var message in getMessages()) { // à cette ligne : Interruption IA : Erreur d'execution : Trop d'opérations éxécutées pour ce tour
	var author = getMessageAuthor(message);
	if (getFarmerID(author) !== getFarmerID()) continue;
	if (getMessageType(message) !== MESSAGE_CUSTOM) continue;
	var content = getMessageParams(message);
	if (typeOf(content) !== TYPE_ARRAY or count(content) !== 2) continue;
	

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extern crate lettre;
extern crate env_logger;

use lettre::email::EmailBuilder;
use lettre::transport::smtp::SmtpTransportBuilder;
use lettre::transport::EmailTransport;
use std::error::Error;


fn main() {

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import torch


def compute_kernel(f, xtr):
    '''
    :param f: neural network with scalar output
    :param xtr: images trainset [P, ...]
    '''
    ktrtr, _, _ = compute_kernels(f, xtr, xtr[:1])
    return ktrtr

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import numpy as np
import functools

def einsum(operand, *tensors):
    op_in, op_out = operand.split('->')
    op_in = op_in.split(',')

    assert all(len(op) == x.ndim for op, x in zip(op_in, tensors))

    op_out = list(op_out)

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# from sympy.functions.special.spherical_harmonics import Znm
# yl(x, y, z) = -(-1)**l * (-(-1)**m if m >=0 else 1) sqrt(4 pi) * Znm(l, m, acos(z), atan2(y, x))
# assuming x**2 + y**2 + z**2 = 1

import jax
import jax.numpy as jnp

@jax.jit
def y0(x, y, z):
    return jnp.stack([

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import inspect

class LINE:
  def __repr__(self):
    return str(inspect.stack()[1].lineno)

__line__ = LINE()

print(__line__)

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