rgreenblatt

import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt

superhuman_coder_accel = 3.6
superhuman_coder_accel_add = superhuman_coder_accel-1
current_accel = 1.05
current_accel_add = current_accel -1

# Define the ODE

rgreenblatt

import os
import subprocess
import json


# %%

subprocess.run(
    [
        "wget",

rgreenblatt

# %%

import os

import openai
import numpy as np
from tqdm import trange

# %%

rgreenblatt

let g:window_key_prefix = "<space>"
" the first value is the key and the second is the new window command
let g:window_key_mappings = [
            \ ["h", "aboveleft vsplit"],
            \ ["j", "belowright split"],
            \ ["k", "aboveleft split"],
            \ ["l", "belowright vsplit"],
            \ [",", 
            \ "let buf = bufnr('%') <bar> tabnew <bar> execute 'buffer' buf"],
            \ [".", ""],

rgreenblatt

const builtin = @import("builtin");
const std = @import("std");
const assert = std.debug.assert;
const log = std.log.scoped(.debug);
const build_options = @import("build_options");
const log_writer = @import("log.zig").Writer{.context = {}};
const c = @import("c.zig");

extern var __debug_info_start: u8;
extern var __debug_info_end: u8;

rgreenblatt

from keras import backend as K
from keras.optimizers import Optimizer
from keras.utils import get_custom_objects


class Padam(Optimizer):
    """Partially adaptive momentum estimation optimizer.
    # Arguments
        lr: float >= 0. Learning rate.
        beta_1: float, 0 < beta < 1. Generally close to 1.