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class GraphWeightsNetwork(nn.Module):
    def __init__ (self, continuous_dim, cat_dim, output_dim=1, hidden_dim=32, conv_depth=1):
        super(GraphMETNetwork, self).__init__()
        
        self.embed_charge = nn.Embedding(3, hidden_dim//4)
        self.embed_pdgid = nn.Embedding(7, hidden_dim//4)
        self.embed_pv = nn.Embedding(8, hidden_dim//4)
        
        self.embed_continuous = nn.Sequential(nn.Linear(continuous_dim,hidden_dim//2),
                                              nn.ELU(),                                             

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32.866 test_corrected_jets_factory  test_jetmet_tools.py:347
└─ 32.866 build  coffea/jetmet_tools/CorrectedJetsFactory.py:275
   ├─ 32.376 __setitem__  awkward1/highlevel.py:966
   │  └─ 32.176 with_field  awkward1/operations/structure.py:438
   │     ├─ 25.561 broadcast_and_apply  awkward1/_util.py:492
   │     │  ├─ 21.474 apply  awkward1/_util.py:549
   │     │  │  ├─ 8.790 apply  awkward1/_util.py:549
   │     │  │  │  ├─ 4.407 getfunction  awkward1/operations/structure.py:500
   │     │  │  │  │  └─ 4.406 of  awkward1/nplike.py:10
   │     │  │  │  │     └─ 4.404 kernels  awkward1/operations/convert.py:532

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#
# Copyright 2015 gRPC authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

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#!/usr/bin/env bash
set -e pipefail

NC='\033[0m'
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'

gpuminer_image="local/gpuminer:latest"
bcnode_image="local/bcnode:latest"

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ARG SERVERBASE=20.09-py3

FROM nvcr.io/nvidia/pytorch:${SERVERBASE}

ENV FORCE_CUDA=1
ARG LIB_WITH_CUDA=ON
ARG NPROC=4

RUN git clone https://github.com/rusty1s/pytorch_cluster.git
RUN pushd pytorch_cluster &&\

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def loadData(fileName):

    # 1. Load Data #
    #fileName = "/uscms/home/dkim2/nobackup/safetodel/nano_mc2017_1-11_Skim.root"
    file = uproot.open(fileName)
    printer.info("file: "+ fileName)

    events = file["Events"].arrays(how="zip")

    mask = events.pfcand_pt > 0

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from coffea import hist
import numpy as np
import random
import string
from tqdm import tqdm

N_ds = 45
N_reg = 3
N_ds_full_syst = 6
N_syst = 53

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import torch
from torch_cmspepr.dataset import TauDataset
import json
from tqdm import tqdm

dataset = TauDataset("/mnt/d/mldata/hgcalml/npzs_all")

out_list = []

for i, data in tqdm(enumerate(dataset[:1000])):

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import torch
from torch_cmspepr.gravnet_model import GravnetModelWithNoiseFilter

model = GravnetModelWithNoiseFilter(input_dim=9, output_dim=6, k=50, signal_threshold=.05)

weights = torch.load("ckpt_train_taus_integrated_noise_Oct20_212115_best_397.pth.tar")

model.load_state_dict(weights["model"])

jitted = torch.jit.script(model)

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import random
from dask.distributed import Client, worker_client, as_completed, secede, rejoin
import pprint
import time

def reduce_chunks(items):
    out = 0
    for item in items:
        out += item #.result()
    return out