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#using for conda environment

pip install environment_kernels
pip install --user ipykernel
python -m ipykernel install --user --name=new_planet

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#find all files that are .png or .jpg and delete them
find . -type f  \( -name "*.jpg" -o -name "*.png" \) -delete

#find all files that are not .png or .jpg and delete them
find . -type f ! \( -name "*.jpg" -o -name "*.png" \) -delete

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from graphviz import Digraph
import torch
from torch.autograd import Variable


def make_dot(var, params=None):
    """ Produces Graphviz representation of PyTorch autograd graph
    Blue nodes are the Variables that require grad, orange are Tensors
    saved for backward in torch.autograd.Function
    Args:

MakGulati

class Environment(object):
    """
    The abstract environment class that is used by all agents. This class has the exact
    same API that OpenAI Gym uses so that integrating with it is trivial. In contrast to the
    OpenAI Gym implementation, this class only defines the abstract methods without any actual
    implementation.

    To implement your own environment, you need to define the following methods:

MakGulati

import argparse
import tensorflow as tf
from tensorflow.keras.datasets import mnist

from ray.tune.integration.keras import TuneReportCallback
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune import Trainable

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import argparse
from tensorflow.keras.datasets import mnist

from ray.tune.integration.keras import TuneReportCallback

parser = argparse.ArgumentParser()
parser.add_argument(
    "--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()

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name: flwr
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _pytorch_select=0.1=cpu_0
  - _tflow_select=2.3.0=mkl
  - absl-py=0.12.0=py38h06a4308_0
  - aiohttp=3.7.4=py38h27cfd23_1
  - astunparse=1.6.3=py_0

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# simple_producer.py

import faust
import numpy as np
import json
from typing import Mapping
from numpy import ndarray
from json import JSONEncoder

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import flwr as fl
import tensorflow as tf
from typing import Optional
from typing import List, Dict, Optional, Tuple
from flwr.common import Scalar
import os, os.path


def weighted_loss_avg(results: List[Tuple[int, float, Optional[float]]]) -> float:
    """Aggregate evaluation results obtained from multiple clients."""