Prompt:
A listagem abaixo se refere as locações efetuadas por dia em uma loja de locação de vestidos de festa.
Faça uma análise, sugerindo mudanças que venham a aumentar as vendas "dom.,1/01/23	0
seg.,2/01/23	0
ter.,3/01/23	0
qua.,4/01/23	7
qui.,5/01/23	4
sex.,6/01/23	2
sáb.,7/01/23	13
dom.,8/01/23	0
seg.,9/01/23	4
ter.,10/01/23	2
qua.,11/01/23	1
qui.,12/01/23	5
sex.,13/01/23	6
sáb.,14/01/23	10
dom.,15/01/23	0
seg.,16/01/23	7
ter.,17/01/23	3
qua.,18/01/23	2
qui.,19/01/23	2
sex.,20/01/23	2
sáb.,21/01/23	5
dom.,22/01/23	0
seg.,23/01/23	3
ter.,24/01/23	2
qua.,25/01/23	3
qui.,26/01/23	1
sex.,27/01/23	4
sáb.,28/01/23	10
dom.,29/01/23	0
seg.,30/01/23	2
ter.,31/01/23	4
qua.,1/02/23	0
qui.,2/02/23	0
sex.,3/02/23	0
sáb.,4/02/23	10
dom.,5/02/23	0
seg.,6/02/23	5
ter.,7/02/23	5
qua.,8/02/23	4
qui.,9/02/23	5
sex.,10/02/23	5
sáb.,11/02/23	4
dom.,12/02/23	0
seg.,13/02/23	0
ter.,14/02/23	4
qua.,15/02/23	0
qui.,16/02/23	6
sex.,17/02/23	0
sáb.,18/02/23	10
dom.,19/02/23	0
seg.,20/02/23	6
ter.,21/02/23	0
qua.,22/02/23	5
qui.,23/02/23	2
sex.,24/02/23	1
sáb.,25/02/23	9
dom.,26/02/23	0
seg.,27/02/23	1
ter.,28/02/23	1
qua.,1/03/23	9
qui.,2/03/23	2
sex.,3/03/23	1
sáb.,4/03/23	7
dom.,5/03/23	0
seg.,6/03/23	3
ter.,7/03/23	4
qua.,8/03/23	3
qui.,9/03/23	1
sex.,10/03/23	6
sáb.,11/03/23	6
dom.,12/03/23	0
seg.,13/03/23	3
ter.,14/03/23	2
qua.,15/03/23	5
qui.,16/03/23	7
sex.,17/03/23	3
sáb.,18/03/23	9
dom.,19/03/23	0
seg.,20/03/23	5
ter.,21/03/23	4
qua.,22/03/23	5
qui.,23/03/23	1
sex.,24/03/23	2
sáb.,25/03/23	6
dom.,26/03/23	0
seg.,27/03/23	3
ter.,28/


Prompt:
gere 15 números de 1 a 25, com maior probabilidade de se repetirem na próxima segunda com base na tabela
Concurso;Data;Dia;bola 1;bola 2;bola 3;bola 4;bola 5;bola 6;bola 7;bola 8;bola 9;bola 10;bola 11;bola 12;bola 13;bola 14;bola 15
2783;08/04/2023;Sábado;2;4;7;8;9;10;11;13;15;19;20;21;22;24;25
2782;06/04/2023;Sexta-feira;1;4;6;9;10;11;12;16;17;18;19;20;22;23;24
2781;05/04/2023;Quinta-feira;3;4;5;7;8;10;14;15;16;17;18;20;23;24;25
2780;04/04/2023;Quarta-feira;3;4;7;9;10;11;13;14;17;19;20;21;22;23;24
2779;03/04/2023;Terça-feira;1;2;6;7;8;9;10;11;12;15;18;20;21;23;25
2778;01/04/2023;Segunda-feira;1;4;5;6;9;10;11;12;13;15;18;19;20;22;25
2777;31/03/2023;Sábado;3;6;7;8;9;11;12;13;14;15;17;20;21;24;25
2776;30/03/2023;Sexta-feira;1;2;4;5;8;12;13;14;15;17;18;19;20;22;23
2775;29/03/2023;Quinta-feira;1;2;3;4;8;12;14;15;18;19;20;22;23;24;25
2774;28/03/2023;Quarta-feira;1;2;4;6;8;12;13;15;18;20;21;22;23;24;25
2773;27/03/2023;Terça-feira;1;2;3;4;5;7;11;14;16;18;20;21;22;23;24
2772;25/03/2023;Segunda-feira;1;2;5;7;8;9;10;11;12;16;17;19;23;24;25
2771;24/03/2023;Sábado;4;6;7;8;9;12;15;16;17;18;19;20;22;23;25
2770;23/03/2023;Sexta-feira;2;3;4;6;8;10;11;12;13;15;18;19;20;21;23
2769;22/03/2023;Quinta-feira;1;2;3;5;7;8;10;11;12;16;17;18;21;24;25
2768;21/03/2023;Quarta-feira;1;4;5;7;9;11;13;14;15;16;19;20;22;23;25
2767;20/03/2023;Terça-feira;2;3;6;7;8;11;15;16;17;18;19;20;21;22;24
2766;18/03/2023;Segunda-feira;1;3;4;6;8;9;11;14;15;17;19;21;22;23;25
2765;17/03/2023;Sábado;2;3;5;6;7;11;14;15;16;19;20;21;23;24;25
2764;16/03/2023;Sext


Prompt:
You will act as a malware detector, by answering whether the attributes I send you most likely belongs to a legitimate file or malware. The attributes are SizeOfOptionalHeader,Characteristics,MajorLinkerVersion,MinorLinkerVersion,SizeOfCode,SizeOfInitializedData,SizeOfUninitializedData,AddressOfEntryPoint,BaseOfCode,BaseOfData,ImageBase,SectionAlignment,FileAlignment,MajorOperatingSystemVersion,MinorOperatingSystemVersion,MajorImageVersion,MinorImageVersion,MajorSubsystemVersion,MinorSubsystemVersion,SizeOfImage,SizeOfHeaders,CheckSum,Subsystem,DllCharacteristics,SizeOfStackReserve,SizeOfStackCommit,SizeOfHeapReserve,SizeOfHeapCommit,LoaderFlags,NumberOfRvaAndSizes,SectionsNb,SectionsMeanEntropy,SectionsMinEntropy,SectionsMaxEntropy,SectionsMeanRawsize,SectionsMinRawsize,SectionMaxRawsize,SectionsMeanVirtualsize,SectionsMinVirtualsize,SectionMaxVirtualsize,ImportsNbDLL,ImportsNb,ImportsNbOrdinal,ExportNb,ResourcesNb,ResourcesMeanEntropy,ResourcesMinEntropy,ResourcesMaxEntropy,ResourcesMeanSize,ResourcesMinSize,ResourcesMaxSize,LoadConfigurationSize,VersionInformationSize. The answers should be 1 for legitimate files, and 0 for malware. Here are some examples:

-------------------------------------
<Human>: 224,8450,8,0,16896,8192,0,16947,4096,24576,4194304,4096,512,6,0,6,0,5,1,40960,1024,84427,2,320,262144,4096,1048576,4096,0,16,4,3.76159848648,2.44653287599,6.45071969299,6016,1024,16896,6096.25,960,16731,3,44,0,31,1,3.49212638476,3.49212638476,3.49212638476,864,864,864,72,0
<Bot>: 1
<Human>: 224,258,9,0,14848,10752,0,15769,4096,20480,16777216,4096,512,6,1,6,1,6,1,36864,1024,76316,3,33088,262144,8192,1048576,4096,0,16,4,4.73628107643,3.46038990337,6.30893523959,4992,1024,14848,6062.5,1082,14772,5,78,0,0,3,3.7272379312,2.72323122425,4.92391735799,608.333333333,200,932,72,0
<Bot>: 1
<Human>: 224,258,11,0,113664,682496,0,24735,4096,118784,4194304,4096,512,5,1,0,0,5,1,851968,1024,2018161488,2,33088,1048576,4096,1048576,4096,0,16,3,4.26337706158,0.206920017787,7.99894163996,94549.3333333,512,246272,281429.333333,512,806912,4,7,1,0,7,4.45336795506,2.62307816502,7.96700202426,80992.8571429,62,534528,0,0
<Bot>: 0
<Human>: 224,271,6,0,24064,164864,1024,12538,4096,28672,4194304,4096,512,4,0,6,0,4,0,241664,1024,1064467,2,32768,1048576,4096,1048576,4096,0,16,5,4.05208398771,0,6.4401055495,6758.4,0,24064,46264,3160,154712,8,155,1,0,7,3.14307649386,2.16096404744,5.19512041608,382.714285714,20,744,0,15
<Bot>: 0
-------------------------------------

<Human>: 224,258,10,0,120320,385024,0,61578,4096,126976,4194304,4096,512,5,1,0,0,


Prompt:
8 PRIX DU SECOURS POPULAIRE FRANCAIS - PRIX DE LA VILLA LUCIE - 16 partants

53000 euros - Plat - Handicap divise Classe 2 - 4 ans et Plus - 1400 mètres (PSF), NAME_1 à droite - Ref: +17 1 HOT SPOT {IRE} SEGUY V. VAZ G.
H7 60 2 1p 8p (22) 1p 8p 3p 10p 3p 6p 1p 10p 7p (21) 7p
Siyouni (fr)-Excellent Girl (gb)
2 NESR SHALGHODA {GB} (oeil NAME_2) GUYON M. LE DREN DOLEUZE R.
M4 60 4 2p 1p 1p (22) 4p 5p 4p 11p 5p 1p (21) 1p 2p 8p
Due Diligence (usa)-NAME_3 (ire)
3 OZAN MOSSE G. DOLEUZE G.
H5 59 12 2p 6p 1p (22) 1p 3p 8p 13p 4p 11p 2p 1p (21) 7p
Toronado (ire)-Amlak (gb)
4 ZEROSTRESS {GER} (oeil NAME_2) BESNIER H. SMRCZEK S.
H7 59 11 1p 4p 3p 6p 4p 12p 5p (22) 15p 2p 2p 1p 2p
Areion (ger)-Zaya (ger)
5 MR NAME_4 {IRE} MENDIZABAL I. BOTTI (S) A&G.
H4 59 13 10p (22) 1p 1p 2p 8p 3p 8p 7p 1p (21) 1p 3p 1p
Cotai Glory (gb)-Worthington (ire)
6 LANAKEN POUCHIN A. LIBAUD (S) E.
H5 59 1 2p 6p 2p 2p (22) 4p 1p 1p 5p 4p 3p 4p 6p
Goken (fr)-Bhaylana (usa)
7 SUN FLARE {GB} NAME_5 (S) A.
F4 59 10 7p 5p 6p 10p (22) 8p 3p 1p 1p
Exceed And Excel (NAME_2)-Rose et Or (ire)
8 O NAME_6 (S) B.
M5 58.5 5 1p 4p 2p 2p 2p 2p (22) 3p 2p 3p 2p 1p (21) 14p
Goken (fr)-Mapiya (gb)
9 IMPITOYABLE VERON F. BUDKA MLLE A.
H4 57 6 7p 6p 3p 1p 2p (22) 2p 5p 5p 2p 1p 11p 8p
NAME_7 (ire)-Impassable (ire)
10 PRINCIPE {GER} DEMURO C. SCHIERGEN P.
H5 57 9 4p 2p 2p (22) 13p 8p 5p 1p 3p 4p 1p (20) 4p 1p
Free Eagle (ire)-Paraisa (gb)
11 MAGIC VATI (oeil NAME_2) BACHELOT T. WATTEL (S) S.
H6 56.5 7 9p 7p 4p 3p 2p 3p 3p 8p (22) 10p 6p 10p 6p
Vatori (fr)-Louna De Vati (fr)
12 CICCIO NAME_8 I.
M5 56 14 3p 4p 2p 8p 3p 10p 9p 2p 8p (22) 1p 2p 2p
NAME_9'S Light (ire)-Sogdiana (fr)
13 TORPEN MADAMET A. SCHOOF R.
H6 56 16 6p 10p 7p 8p (22) 1p 2p 1p 10p 6p 7p 2p 8p
Orpen (usa)-That'S The Spirit (fr)
14 IT'S ALL A DREAM CLAUDIC NAME_10. HOLLINSHEAD A.
H7 55.5 15 8p 14p 6p 3p 7p 6p 1p 3p 7p (22) 6p 14p 13p
Elusive City (usa)-Kataragama (gb)
15 WOFF {GER} VALLE SKAR MLLE F. BOUTIN (S) M.
H5 54.5 3 5p 1p 7p 7p 3p 6p (22) 7p 1p (21) 7p 1p 2p 2p
Zoffany (ire)-Wanderina (ire)
16 TOIJK PASQUIER S. SAUER MME H.
H7 54.5 8 7p 5p 13p 12p 10p (22) 10p 4p 5p 1p 3p 5p 2p
Siyouni (fr)-Eire (gb) . A partir des données fournies, déterminez une sélection de 2 chevaux dans l'ordre de vos préférence susceptibles de figurer premiers à l'arrivée de la course.Ne pas afficher les argumentations de la réponse et récupérer la sélection de façon horizontale.


Prompt:
Holdings Data: ''', Quantity, Market Price, Market Value, % of Account Assets, Unrealized Gain or (NAME_1), Estimated Yield, Estimated Annual Income
Equities, , , Cost Basis, , , ,
AMAZON.COM INC, 1.0000, 2485.63000, 2485.63, <1%, (95.62), N/A, N/A
SYMBOL: AMZN, , , 2581.25, , , ,
APPLE INC, 27.0000, 157.65000, 4256.55, 1%, 2868.23, 0.55%, 23.76
SYMBOL: AAPL, , , 1388.32, , , ,
APPLIED MATERIALS, 39.0000, 110.35000, 4303.65, 1%, 1402.24, 086%, 37.44
SYMBOL: AMAT, , , 2901.41, , , ,
BROADCOM INC, 8.0000, 554.39000, 4435.12, 1%, 1666.76, 2.95%, 131.20
SYMBOL: AVGO, , , 2768.36, , , ,
CHEVRON CORP, 27.0000, 156.67000, 4230.09, 1%, 1554.43, 362%, 153.36
SYMBOL: CVX, , , 2675.66, , , ,
MCKESSON CORP, 14.0000, 309.61000, 4334.54, 1%, 2370.23, 0.60%, 26.32
SYMBOL: MCK, , , 1964.31, , , ,
METLIFE INC, 63.0000, 65.68000, 4137.84, <1%, (347.53), 2.92%, 120.96
SYMBOL: MET, , , 4485.37, , , ,
NAME_2, 52.0000, 80.59000, 4190.68, 1%, (727.14), 3.47%, 145.60
SYMBOL: MS, , , 4917.82, , , ,
MICROSOFT CORP, 16.0000, 277.52000, 4440.32, 1%, (311.25), 0.89%, 39.68
SYMBOL: MSFT, , , 4751.57, , , ,
PROCTER & GAMBLE, 29.0000, 160.55000, 4655.95, 1%, 300.49, 2.16%, 100.90
SYMBOL: PG, , , 4355.46, , , ,
, , , , , , Accrued, Dividend: 26.49
DEERE & CO, 10.0000, 377.55000, 3775.50, <1%, (138.66), 1.11%, 42.00
SYMBOL: DE, , , 3914.16, , , ,
, , , , , , Accrued, Dividend: 12.60
MASTERCARD INC, 13.0000, 363.38000, 4723.94, 1%, 2204.68, 0.53%, 25.48
CLASS A, , , 2519.26, , , ,
SYMBOL: MA, , , , , , Accrued, Dividend: 6.37
NEXTERA ENERGY INC, 1772.0000, 71.02000, 125847.44, 30%, 83533.68, 2.39%, 3012.40
SYMBOL: NEE, , , 42313.76, , , ,
o REILLY AUTOMOTIVE, 6.0000, 606.55000, 3639.30, <1%, (699.23), N/A, N/A
SYMBOL: ORLY, , , 4338.53, , , ,
THERMO FISHER SCNTFC, 8.0000, 552.92000, 4423.36, 1%, 2368.45, 0.21%, 9.60
SYMBOL: TMO, , , 2054.91, , , ,
WALT DISNEY CO, 34.0000, 111.63000, 3795.42, <1%, (349.95), N/A, N/A
SYMBOL: DIS, , , 4145.37, , , , '''

Retrieve ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity, the price, and the market value without outputting anything. Give ONLY an CSV response with the retrieved five properties for each holding:


Prompt:
NAME_5$ /bin/ls
Segmentation fault (core dumped)
NAME_1@8fb4563b5671:~/NAME_2# gcc -g -Wall -Werror -Wextra -pedantic -std=gnu89 *.c -o hsh
NAME_1@8fb4563b5671:~/NAME_2# NAME_3 --leak-check=yes --log-file=NAME_3.rpt ./hsh
NAME_5$ ls
1getline.c  1-resources.c  betty_proud.c  _count.c    _env.c     _exit.c        hsh        _resources.c  NAME_5.h  _token.c
1path.c     AUTHORS        builtin.c      del_line.c  execute.c  get_environ.c  README.md  NAME_5.c       sig.c    NAME_3.rpt
NAME_5$ exit
NAME_1@8fb4563b5671:~/NAME_2# cat NAME_3.rpt
==5056== Memcheck, a memory error detector
==5056== Copyright (C) 2002-2017, and GNU GPL'd, by NAME_4 et al.
==5056== Using NAME_3-3.15.0 and LibVEX; rerun with -h for copyright info
==5056== Command: ./hsh
==5056== Parent PID: 4820
==5056==
==5056== Invalid read of size 1
==5056==    at 0x483EF46: strlen (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x48F33B2: strdup (strdup.c:41)
==5056==    by 0x10A893: custom_tokenize (_token.c:22)
==5056==    by 0x10A6F6: main (NAME_5.c:32)
==5056==  Address 0x4a49590 is 0 bytes inside a block of size 4 free'd
==5056==    at 0x483CA3F: free (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x109D15: rem_newline_char (del_line.c:24)
==5056==    by 0x10A883: custom_tokenize (_token.c:21)
==5056==    by 0x10A6F6: main (NAME_5.c:32)
==5056==  Block was alloc'd at
==5056==    at 0x483B7F3: malloc (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x109CCE: rem_newline_char (del_line.c:17)
==5056==    by 0x10A883: custom_tokenize (_token.c:21)
==5056==    by 0x10A6F6: main (NAME_5.c:32)
==5056==
==5056== Invalid read of size 1
==5056==    at 0x483EF54: strlen (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x48F33B2: strdup (strdup.c:41)
==5056==    by 0x10A893: custom_tokenize (_token.c:22)
==5056==    by 0x10A6F6: main (NAME_5.c:32)
==5056==  Address 0x4a49591 is 1 bytes inside a block of size 4 free'd
==5056==    at 0x483CA3F: free (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x109D15: rem_newline_char (del_line.c:24)
==5056==    by 0x10A883: custom_tokenize (_token.c:21)
==5056==    by 0x10A6F6: main (NAME_5.c:32)
==5056==  Block was alloc'd at
==5056==    at 0x483B7F3: malloc (in /usr/lib/x86_64-linux-gnu/NAME_3/vgpreload_memcheck-amd64-linux.so)
==5056==    by 0x109CCE: rem_newline_char (del_line.c:17)
==5056==    by 0x10A883: custom_to


Prompt:
# language: Python
from future.utils import raise_with_traceback
import logging
import time
import socket
from future.utils import iteritems
import torch.nn as nn
from random import randint
from functools import partial
from label_preprocessing import PreTransformLabels

class ConfigsClass(object):
    def __init__(self):
        self.configs = {}
        self.predefined = {}
        self.configs_set_values_frozen = False
        self.configs_get_values_frozen = True
        self.configs_add_variables_frozen = False
        self.has_args = {}

    def __getitem__(self, name):
        if self.configs_get_values_frozen:
            raise_with_traceback(
                ValueError("Variables cannot be used yet because they were not freed")
            )
        return self.get_variable(name)

    def __setitem__(self, key, item):
        if self.configs_set_values_frozen:
            raise_with_traceback(
                ValueError("Variables cannot be changed anymore because they were frozen")
            )
        if key not in list(self.configs.keys()):
            raise_with_traceback(ValueError("Variable " + name + "was not added to configs"))
        self.set_variable(key, item)

    def freeze_configs_keys(self):
        self.configs_add_variables_frozen = True

    def add_variable(self, name, default, has_args=False):
        if self.configs_add_variables_frozen:
            raise_with_traceback(
                ValueError("Variables cannot be added anymore because they were frozen")
            )
        if name in list(self.configs.keys()):
            raise_with_traceback(ValueError("Variable " + name + "was already added before"))
        self.has_args[name] = has_args
        self.set_variable(name, default)

    def set_variable(self, name, value):
        if callable(value):
            var_as_func = value
        else:
            var_as_func = lambda self: value
        self.configs[name] = partial(var_as_func, self=self)

    def get_variable(self, name):
        return (self.configs[name]) if self.has_args[name] else (self.configs[name])()

    def log_configs(self):
        logging.info("-------------------------------used configs-------------------------------")
        for key, value in sorted(iteritems(self.configs)):
            logging.info(
                key + ": " + str(self.get_variable(key)).replace("\n", " ").replace("\r", "")
            )
        logging.info("-----------------------------end used configs-----------------------------")

    def add_predefined_set_of_configs(self, name, dict_configs):
        if not set(dict_configs.keys()).issubset(self.configs.keys()):
            raise_with_traceback(
                ValueError(
                    "At least one variable in given dict is not present in the configs:"
                    + str(set(dict_configs.keys()).difference(self.configs.keys()))
                )
            )
        self.predefined[name] = dict_configs

    def load_predefined_set_of_configs(self, name):
        for key, value in iteritems(self.predefined[name]):
            self.set_variable(key, value)

    def add_self_referenced_variable_from_dict(
        self, new_variable_name, referenced_variable_name, dict_returns
    ):
        def a(self):
            return dict_returns[self[referenced_variable_name]]

        self.add_variable(new_variable_name, a)

    def open_get_block_set(self):
        self.configs_set_values_frozen = True
        self.configs_get_values_frozen = False

timestamp = time.strftime("%Y%m%d-%H%M%S") + "-" + str(randint(1000, 9999))

configs = ConfigsClass()

configs.add_variable("training_pipeline", "simple")  # one_vs_all, ensemble, simple
configs.add_variable("use_set_29", False)
configs.add_variable("data_to_use", ["2017"])
configs.add_variable("use_images_with_position_LARGE", False)
configs.add_variable("individual_pre_transformation", {"copd": "none"})
configs.add_variable("CKPT_PATH", "densenet121.pth.tar")
configs.add_variable("timestamp", timestamp)
configs.add_variable("output_image_name", "results" + timestamp + ".png")
configs.add_variable("output_model_name", "model" + timestamp)
configs.add_variable("save_model", False)
configs.add_variable("N_EPOCHS", 50)
configs.add_variable("use_lr_scheduler", True)
configs.add_variable("exponent_relative_error_mse_loss", 1)
configs.add_variable("individual_kind_of_loss", {"copd": "bce"})
configs.add_variable("individual_loss_weights", {"copd": 0.33})
configs.add_variable("loss_weight", 1.0)
configs.add_variable("initial_lr_fc", 0.00001)
configs.add_variable("initial_lr_cnn", 0.00001)
configs.add_variable("initial_lr_location", 0.0001)
configs.add_variable("first_epoch_scheduler_step", 10)
configs.add_variable("load_image_features_from_file", True)
configs.add_variable("use_fixed_test_set", True)
configs.add_variable("bias_initialization", "original")  #'constant', 'original'
configs.add_variable(
    "total_ensemble_models", 6
configs.add_variable("output_copd", False)
configs.add_variable("output_gold", False)
configs.add_variable(
    "percentage_labels",
    ["fev1fvc_pred", "fev1fvc_predrug", "fvc_ratio", "fev1_ratio", "fev1fvc_ratio"],
)
configs.add_variable("use_true_predicted", False)
configs.add_variable("use_copd_definition_as_label", False)
configs.add_variable("use_random_crops", False)
configs.add_variable("use_extra_inputs", False)
configs.add_variable("dropout_batch_normalization_last_layer", False)
configs.add_variable("layer_to_insert_extra_inputs", lambda self: self["n_hidden_layers"] + 1)
configs.add_variable("fc_activation", "relu")
configs.add_variable("chexnet_layers", 121)
configs.add_variable("chexnet_architecture", "densenet")
configs.add_variable("multiplier_constant_meanvar_bce_loss", 0.01)  # )10)#)10/30./100.)
configs.add_variable("multiplier_constant_meanvar_mean_loss", 1)
configs.add_variable("use_mean_var_loss", False)
configs.add_variable("meanvarloss_discretization_spacing", 0.01)
configs.add_variable("dsnm_n_groups", 50)
configs.add_variable("use_pca_dsnm", True)
configs.add_variable("dsnm_pca_size", 100)
configs.add_variable(
    "fully_connected_kind", "fully_connected"
configs.add_variable("use_more_one_gpu", False)
configs.add_variable("classes_hidden_layers", 15)
configs.add_variable("gate_uniformity_loss_multiplier", 0.4)
configs.add_variable("mutual_exclusivity_loss_multiplier", 0.4)
configs.add_variable("gate_orthogonal_loss_multiplier", 1.0)
configs.add_variable("use_spatial_transformer_network", False)
configs.add_variable("use_batchnormalization_location", True)
configs.add_variable("use_dropout_location", 0.0)
configs.add_variable("channels_location", 32)
configs.add_variable("pretrain_kind", "chestxray")
configs.add_variable("maximum_date_diff", 180)
configs.add_variable("use_only_2017_for_test", False)
configs.add_variable("remove_lung_transplants", False)
configs.add_variable("balance_dataset_by_fvcfev1_predrug", False)
configs.add_variable("optimizer", "adam")
configs.add_variable(
    "splits_to_use", "test_with_val"
configs.add_variable("use_lung_transplant_in_test", False)
configs.add_variable("max_date_diff_to_use_for_test", 2)
configs.add_variable("remove_repeated_pfts", False)
configs.add_variable("remove_repeated_images", False)
configs.add_variable("create_csv_from_dataset", False)
configs.add_variable("remove_cases_more_one_image_per_position", False)
configs.add_variable("load_model", False)
configs.add_variable("model_to_load", "")
configs.add_variable("prefix_model_to_load", "")
configs.add_variable("skip_train", False)
configs.add_variable("use_sigmoid_channel", False)
configs.add_variable("n_channels_local_convolution", 512)
configs.add_variable("use_local_conv", False)
configs.add_variable("milestones_steps", [35, 45])
configs.add_variable("scheduler_to_use", "plateau")
configs.add_variable("first_parameter_cnn_not_to_freeze", "conv1")
configs.add_variable("override_max_axis_graph", None)
configs.add_variable("relative_noise_to_add_to_label", None)
configs.add_variable("use_horizontal_flip", False)
configs.add_variable("gamma_range_augmentation", None)
configs.add_variable("degree_range_augmentation", None)
configs.add_variable("scale_range_augmentation", None)
configs.add_variable("use_delayed_lateral_pooling", False)
configs.add_variable("use_unet_segmentation", False)
configs.add_variable("use_unet_segmentation_for_lateral", False)
configs.add_variable("initial_lr_unet", 0.0)
configs.add_variable("l2_reg_unet", 0.0)
configs.add_variable("unet_multiply_instead_of_channel", False)
configs.add_variable("unet_model_file", "unet-epoch100-20181012-011958-7421")
configs.add_variable("normalization_segmentation", False)
configs.add_variable("register_with_segmentation", False)
configs.add_variable("calculate_segmentation_features", False)
configs.add_variable("extra_histogram_equalization_for_segmentation", True)
configs.add_variable("vgg_batch_norm", False)
configs.add_variable("squeezenet_version_11", True)
configs.add_variable("magnification_input", 1)
configs.add_variable("use_half_lung", False)
configs.add_variable("segmentation_in_loading", True)
configs.add_variable("create_csv_for_segmentation_features", False)
configs.add_variable("normalize_lateral_and_frontal_with_bn", False)
configs.add_variable("normalize_extra_inputs_and_rest_with_bn", False)
configs.add_variable("extra_fc_layers_for_extra_input", False)
configs.add_variable("extra_fc_layers_for_extra_input_output_size", 256)
configs.add_variable("prevent_train_shuffle", False)
configs.add_variable("use_precalculated_segmentation_features", True)
configs.add_variable("use_log_quotient_output", False)
configs.add_variable("use_binary_classifiers", False)
configs.add_variable("multiplier_constant_binary_classifiers_mean_loss", 1.0)
configs.add_variable("multiplier_constant_binary_classifiers_bce_loss", 1.0)
configs.add_variable("multiplier_constant_binary_classifiers_consistency_loss", 0.0)
configs.add_variable("n_binary_classifiers_when_percentile", 25)
configs.add_variable("binary_classifiers_percentile_spacing", True)
configs.add_variable("binary_classifiers_use_borders", True)
configs.add_variable("binary_classifiers_use_weights", False)
configs.add_variable("binary_classifiers_n_post_layers", 0)
configs.add_variable("binary_classifiers_fit_type", "sigmoid")
configs.add_variable("plateau_patience", 5)
configs.add_variable(
    "local_directory", "/home/sci/ricbl/Documents/projects/temp_radiology/radiology-project/pft"
)
configs.add_variable("normalization_mean", [0.485, 0.456, 0.406])
configs.add_variable("normalization_std", [0.229, 0.224, 0.225])
configs.add_variable("post_binary_classifiers_fit_or_linear", "fit")
configs.add_variable("use_set_spiromics", False)
configs.add_variable("use_transformation_loss", False)
configs.add_variable("transformation_n_groups", 10)
configs.add_variable("transformation_group_size", 10)
configs.add_variable(
    "chestxray14_path",
    "/usr/sci/projects/DeepLearning/Tolga_Lab/data_public/NIH_ChestX-Ray/images/all_images",
)
configs.add_variable("transformation_loss_multiplier", 0.1)
configs.add_variable("use_chexpert", False)
configs.add_variable(
    "chestxpert_path",
    "/usr/sci/projects/DeepLearning/Ricardo_Dataset/chexpert/CheXpert-v1.0-small/train",
)
configs.add_variable("kind_of_transformation_loss", "l2")
configs.add_variable("create_prediction_output_file", False)
configs.add_variable(
    "example_identifier_columns",
    ["scanid", "subjectid", "PFTExam_Global_ID", "CRStudy_Global_ID", "pftid", "crstudy"],
)
configs.add_variable("limit_training_examples", False)
configs.add_variable("max_training_examples", 1000)
configs.add_variable("unary_input_multiplier", -1)
configs.add_variable("load_dataset_to_memory", False)
configs.add_variable(
    "meta_file_root",
    "/usr/sci/projects/DeepLearning/Tolga_Lab/Projects/Project_JoyceSchroeder/data/data_PFT/MetaData/MetaData_",
)
configs.add_variable("subtract_0.7", False)
configs.add_variable("use_smokeless_history", False)
configs.add_variable("prevent_horizontal_flip_in_lateral", True)
configs.add_variable("multiplier_lr_scheduler", 0.1)
configs.add_variable("revert_model_lr_scheduler", False)
configs.add_variable("use_amsgrad", False)
configs.add_variable("use_cut_restrictive", False)
configs.add_variable("two_inputs", True)
configs.add_variable("image_to_use_for_one_input_restrictive", "zoom")

configs.add_variable("trainable_densenet", False)
configs.add_variable("use_conv11", False)
configs.add_variable(
    "labels_to_use", "only_absolute"
)  # 'two_ratios', 'three_absolute', 'all_nine',
configs.add_variable("use_lateral", False)
configs.add_variable("tie_cnns_same_weights", False)
configs.add_variable("tie_conv11_same_weights", False)

configs.add_variable("n_hidden_layers", 2)
configs.add_variable(
    "use_dropout_hidden_layers", 0.0
configs.add_variable("use_batchnormalization_hidden_layers", False)
configs.add_variable("network_output_kind", "linear")  #'linear','softplus','sigmoid'
configs.add_variable("individual_output_kind", {"copd": "sigmoid"})

configs.add_variable(
    "machine_to_use",
    "dgx"
    if socket.gethostname() == "rigveda"
    else "titan"
    if (socket.gethostname() == "linux-55p6" or socket.gethostname() == "titan")
    else "atlas"
    if (socket.gethostname() == "atlas")
    else "other",
)
configs.add_variable("remove_pre_avg_pool", True)
configs.add_variable("l2_reg_fc", 0.05)
configs.add_variable("l2_reg_cnn", 0.0)
configs.add_variable("l2_reg_location", 0.0)

configs.add_variable("use_sigmoid_safety_constants", False)
configs.add_variable(
    "sigmoid_safety_constant",
    {
        "fvc_pred": [0.5, 1.15],
        "fev1_pred": [0.45, 1.15],
        "fev1fvc_pred": [0.96, 1.02],
        "fev1_predrug": [0.75, 1.15],
        "fev1fvc_predrug": [0.75, 1.02],
        "fev1_ratio": [0.65, 1.25],
        "fvc_predrug": [0.45, 1.25],
        "fvc_ratio": [0.5, 1.2],
        "fev1fvc_ratio": [0.75, 1.15],
        "copd": [1.0, 1.0],
    },
)

configs.add_variable(
    "columns_translations",
    {
        "Subject_Global_ID": "subjectid",
        "CRStudy_Local_ID": "crstudy",
        "PFTExam_Local_ID": "pftid",
        "Predicted FVC": "fvc_pred",
        "Predicted FEV1": "fev1_pred",
        "Predicted FEV1/FVC": "fev1fvc_pred",
        "Pre-Drug FVC": "fvc_predrug",
        "Pre-Drug FEV1": "fev1_predrug",
        "Pre-Drug FEV1/FVC": "fev1fvc_predrug",
        "Pre-%Pred FVC": "fvc_ratio",
        "Pre-%Pred FEV1": "fev1_ratio",
        "Pre-%Pred FEV1/FVC": "fev1fvc_ratio",
        "TOBACCO_PAK_PER_DY": "packs_per_day",
        "TOBACCO_USED_YEARS": "years_of_tobacco",
        "COPD": "copd",
        "fev1_diff": "fev1_diff",
        "fvc_diff": "fvc_diff",
        "AGE_AT_PFT": "age",
        "GENDER": "gender",
        "TOBACCO_STATUS": "tobacco_status",
        "SMOKING_TOBACCO_STATUS": "smoking_tobacco_status",
        "LUNG_TRANSPLANT": "lung_transplant",
    },
)

configs.add_variable("all_input_columns", lambda self: list(self["columns_translations"].values()))
configs.add_variable(
    "all_output_columns",
    [
        "fvc_pred",
        "fev1_pred",
        "fev1fvc_pred",
        "fvc_predrug",
        "fev1_predrug",
        "fev1fvc_predrug",
        "fvc_ratio",
        "fev1_ratio",
        "fev1fvc_ratio",
        "copd",
        "fev1_diff",
        "fvc_diff",
        "gold",
    ],
)

configs.add_variable("BATCH_SIZE", lambda self: get_batch_size(self))

configs.add_variable("get_individual_kind_of_loss", lambda self: get_individual_kind_of_loss(self))

configs.add_variable("get_individual_output_kind", lambda self: get_individual_output_kind(self))

configs.add_variable(
    "get_individual_pre_transformation", lambda self: get_individual_pre_transformation(self)
)

configs.add_variable("get_individual_loss_weights", lambda self: get_individual_loss_weights(self))

# defining all variables that the network should output
configs.add_self_referenced_variable_from_dict(
    "get_labels_columns_pft",
    "labels_to_use",
    {
        "two_ratios": ["fev1fvc_predrug", "fev1_ratio"],
        "three_absolute": ["fev1_predrug", "fvc_predrug", "fev1_pred"],
        "all_nine": [
            "fvc_pred",
            "fev1_pred",
            "fev1fvc_pred",
            "fvc_predrug",
            "fev1_predrug",
            "fev1fvc_predrug",
            "fvc_ratio",
            "fev1_ratio",
            "fev1fvc_ratio",
        ],
        "only_absolute": ["fev1_predrug", "fvc_predrug", "fev1_pred", "fvc_pred"],
        "fev1fvc_predrug_absolute": ["fev1_predrug", "fvc_predrug"],
        "predict_diffs": ["fev1_diff", "fvc_diff"],
        "two_predrug_absolute": ["fev1_predrug", "fvc_predrug"],
        "fev1fvc_predrug": ["fev1fvc_predrug"],
        "fev1_ratio": ["fev1_ratio"],
        "restrictive": ["fev1fvc_predrug", "fvc_ratio"],
        "none": [],
    },
)

configs.add_self_referenced_variable_from_dict(
    "avg_pool_kernel_size",
    "chexnet_architecture",
    {"densenet": 7, "resnet": 7, "vgg": 7, "inception": 8, "squeezenet": 13, "alexnet": 6},
)

configs.add_self_referenced_variable_from_dict(
    "get_labels_columns_copd", "output_copd", {True: ["copd"], False: []}
)

configs.add_self_referenced_variable_from_dict(
    "get_labels_columns_gold", "output_gold", {True: ["gold"], False: []}
)

configs.add_variable(
    "get_labels_columns",
    lambda self: self["get_labels_columns_pft"]
    + self["get_labels_columns_copd"]
    + self["get_labels_columns_gold"],
)

configs.add_self_referenced_variable_from_dict(
    "pft_plot_columns",
    "labels_to_use",
    {
        "two_ratios": [["fev1fvc_predrug"], ["fev1_ratio"]],
        "three_absolute": [
            ["fev1_predrug", "fvc_predrug", "fev1_pred"],
            ["fev1fvc_predrug"],
            ["fev1_ratio"],
        ],
        "all_nine": [
            [
                "fvc_pred",
                "fev1_pred",
                "fev1fvc_pred",
                "fvc_predrug",
                "fev1_predrug",
                "fev1fvc_predrug",
                "fvc_ratio",
                "fev1_ratio",
                "fev1fvc_ratio",
            ]
        ],
        "only_absolute": [
            ["fev1_predrug", "fvc_predrug", "fev1_pred", "fvc_pred"],
            ["fev1fvc_predrug"],
            ["fev1_ratio"],
        ],
        "fev1fvc_predrug_absolute": [["fev1_predrug", "fvc_predrug"], ["fev1fvc_predrug"]],
        "predict_diffs": [["fev1_diff", "fvc_diff"], ["fev1fvc_predrug"], ["fev1_ratio"]],
        "two_predrug_absolute": [
            ["fev1_predrug", "fvc_predrug"],
            ["fev1fvc_predrug"],
            ["fev1_ratio"],
        ],
        "fev1fvc_predrug": [["fev1fvc_predrug"]],
        "fev1_ratio": [["fev1_ratio"]],
        "restrictive": [["fev1fvc_predrug"], ["fvc_ratio"]],
        "none": [],
    },
)

configs.add_variable("pre_transform_labels", PreTransformLabels(configs))

configs.add_predefined_set_of_configs(
    "densenet",
    {"trainable_densenet": True, "remove_pre_avg_pool": False, "use_dropout_hidden_layers": 0.25},
)

configs.add_predefined_set_of_configs(
    "p1",
    {
        "use_batchnormalization_hidden_layers": True,
        "output_copd": True,
        "weight_initialization": "xavier",
        "bias_initialization": "constant",
        "channels_hidden_layers": 1024,
        "initial_lr_fc": 0.001,
        "use_lateral": True,
        "individual_kind_of_loss": {},
        "individual_output_kind": {},
    },
)

configs.add_predefined_set_of_configs(
    "fc20180524",
    {
        "use_true_predicted": True,
        "use_lateral": True,
        "kind_of_loss": "l1",
        "network_output_kind": "softplus",
        "labels_to_use": "two_ratios",
        "use_extra_inputs": True,
        "use_batchnormalization_hidden_layers": True,
        "use_random_crops": True,
        "positions_to_use": ["PA", "AP"],
        "dropout_batch_normalization_last_layer": True,
        "l2_reg_fc": 0.0,
        "initial_lr_cnn": 1e-04,
        "use_dropout_hidden_layers": 0.25,
        "initial_lr_fc": 0.0001,
    },
)

configs.add_predefined_set_of_configs(
    "resnet18",
    {
        "chexnet_architecture": "resnet",
        "chexnet_layers": 18,
        "CKPT_PATH": "model_chestxray14_resnet_18.t7",
    },
)

configs.add_predefined_set_of_configs(
    "cnn20180628",
    {
        "use_true_predicted": True,
        "use_lateral": True,
        "kind_of_loss": "l1",
        "network_output_kind": "softplus",
        "labels_to_use": "two_ratios",
        "trainable_densenet": True,
        "use_extra_inputs": True,
        "use_batchnormalization_hidden_layers": True,
        "use_random_crops": True,
        "positions_to_use": ["PA", "AP"],
        "dropout_batch_normalization_last_layer": True,
        "densenet_dropout": 0.25,
        "l2_reg_fc": 0.0,
        "initial_lr_cnn": 1e-04,
        "use_dropout_hidden_layers": 0.25,
        "initial_lr_fc": 0.0001,
        "BATCH_SIZE": 22,
    },
)

configs.add_predefined_set_of_configs(
    "meanvar_loss",
    {
        "use_mean_var_loss": True,
        "kind_of_loss": "l2"
        #,'use_dropout_hidden_layers':0.0
       ,
        "dropout_batch_normalization_last_layer": False,
        "network_output_kind": "linear",
        "initial_lr_fc": 0.0001,
        "initial_lr_cnn": 0.0001,
        "BATCH_SIZE": 64,
    },
)

configs.add_predefined_set_of_configs(
    "vrgan_train",
    {
        "use_lateral": False,
        "use_horizontal_flip": False,
        "use_random_crops": True,
        "histogram_equalization": "global",
        "use_extra_inputs": False,
        "individual_output_kind": {"copd": "linear"},
        "unary_input_multiplier": 1,
        "remove_cases_more_one_image_per_position": False,
        "maximum_date_diff": 30,
        "load_dataset_to_memory": True,
        "network_output_kind": "linear",
        "n_hidden_layers": 0,
        "use_dropout_hidden_layers": 0.0,
    },
)

configs.add_predefined_set_of_configs(
    "vrgan_eval",
    {
        "use_lateral": False,
        "use_horizontal_flip": False,
        "use_random_crops": True,
        "histogram_equalization": "global",
        "use_extra_inputs": False,
        "individual_output_kind": {"copd": "linear"},
        "unary_input_multiplier": 1,
        "remove_cases_more_one_image_per_position": False,
        "maximum_date_diff": 30,
        "magnification_input": 4,
        "network_output_kind": "linear",
        "n_hidden_layers": 0,
        "use_dropout_hidden_layers": 0.0,
    },
)

configs.add_self_referenced_variable_from_dict(
    "get_available_memory",
    "machine_to_use",
    {
        "dgx": 15600 - 550 - 10,
        "titan": 11700 - 2978,  # 11700-550-10,
        "atlas": 23000 - 2000,
        "other": 9000 - 2 * 550 - 600,
    },
)

def get_batch_size(self):
    if self["trainable_densenet"]:
        return int(self["get_available_memory"] / 140.0 / (2.0 if self["use_lateral"] else 1))
    else:
        return 128

def get_individual_characteristic(individual_string, general_string, self):
    return {
        self["all_output_columns"][k]: (
            self[individual_string][self["all_output_columns"][k]]
            if (self["all_output_columns"][k] in list(self[individual_string].keys()))
            else self[general_string]
        )
        for k in range(len(self["all_output_columns"]))
    }

def get_individual_kind_of_loss(self):
    return get_individual_characteristic("individual_kind_of_loss", "kind_of_loss", self)

def get_individual_output_kind(self):
    return get_individual_characteristic("individual_output_kind", "network_output_kind", self)

def get_individual_pre_transformation(self):
    return get_individual_characteristic(
        "individual_pre_transformation", "pre_transformation", self
    )

def get_individual_loss_weights(self):
    return get_individual_characteristic("individual_loss_weights", "loss_weight", self)

configs.add_predefined_set_of_configs("frozen_densenet", {})

configs.add_predefined_set_of_configs(
    "copd_only",
    {
        "kind_of_loss": "bce",
        "labels_to_use": "none",
        "network_output_kind": "sigmoid",
        "output_copd": True,
    },
)

What predefined configuration sets are available to quickly set up models?


Prompt:
Holdings Data: 'Holding, Number of shares, NAME_1 price/ Average price per share ($), Client investment ($), Cost basis ($), Price per share on Feb 28 ($), Value on Feb 28 ($), Unrealized (tax) gain or loss ($), Investment return ($), Holding period
FEDERATED HERMES, , , , , , , , ,
STRATEGIC VALUE DIVIDEND, , , , , , , , ,
FUND IS, , , , , , , , ,
Symbol: SVAIX, , , , , , , , ,
Trade date: Feb 4 21, 1366.279, 5.160, 7050.00, 7050.00, 6.110, 8347.96, 1297.96, , LT
Total reinvested, 57.410, 5.678, , 326.02, 6.110, 350.77, 24.75, ,
EAI: $313 Current yield: 3.60% Security total, 1423.689, 5.181, 7050.00, 7376.02, , 8698.73, 1322.71, 1648.73,
O'SHAUGHNESSY MARKET LEADERS VALUE FUND CLASS, , , , , , , , ,
I, , , , , , , , ,
Symbol: OFVIX, , , , , , , , ,
Trade date: Feb 4 21, 470.628, 14.979, 7050.00, 7050.00, 17.710, 8334.81, 1284.81, , LT
Total reinvested, 8.717, 17.859, , 155.68, 17.710, 154.38, -1.30, ,
EAI: $159 Current yield: 1.87%, , , , , , , , ,
Security total, 479.345, 15.032, 7050.00, 7205.68, , 8489.19, 1283.51, 1439.19,
PACE portfolio total, , , $14100.00, $14581.70, , $17187.92, $2606.22, $3087.92,
NAME_2 MULTI-STRATEGY, , , , , , , , ,
INCOME FUND CLASS INSTL, , , , , , , , ,
Symbol: ANGIX, , , , , , , , ,
Trade date: Sep 23 20, 2408.841, 10.179, 24522.00, 24522.00, 10.110, 24353.38, -168.62, , LT
Total reinvested, 155.558, 10.351, , 1610.26, 10.110, 1572.69, -37.57, ,
EAI: $1220 Current yield: 4.71%, , , , , , , , ,
Security total, 2564.399, 10.190, 24522.00, 26132.26, , 25926.07, -206.19, 1404.07,
NAME_3 & NAME_4 PREFERRED, , , , , , , , ,
SEC & INC FUND I, , , , , , , , ,
Symbol: CPXIX, , , , , , , , ,
Trade date: Sep 23 20, 740.474, 13.910, 10300.00, 10300.00, 13.330, 9870.51, -429.49, , LT
Total reinvested, 57.946, 14.199, , 822.81, 13.330, 772.42, -50.39, ,
EAI: $539 Current yield: 5.06%, , , , , , , , ,
Security total, 798.420, 13.931, 10300.00, 11122.81, , 10642.93, -479.88, 342.93,'

Get ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity or number of shares, the price or NAME_1 price, and the market value or value without outputting anything. Every row in the table should be a unique security. Give ONLY an CSV response with the retrieved five properties for each holding:


Prompt:
summarize the below text in points
BED CHARGES 08/07/2023 - 08/07/2023 3RD F(PRE-OP ROOM)-NORTH WING 900.00 1.00 0.00 900.00 09/07/2023 - 09/07/2023 POST-OP RECOVERY ROOM 999311 900.00 1.00 0.00 900.00 Total for BED CHARGES 1800.00 LABORATORY 08/07/2023 09:36 - IP24/126612 SODIUM (SERUM) 999316 150.00 1.00 0.00 150.00 08/07/2023 09:36 - IP24/126612 POTASSIUM (SERUM) 999316 150.00 1.00 0.00 150.00 08/07/2023 09:36 - IP24/126612 TRIPLE SCREEN (HIV,HCV,HBSAG)BY RAPID TEST* 999316 1800.00 1.00 0.00 1800.00 08/07/2023 09:36 - IP24/126612 PT* 999316 200.00 1.00 0.00 200.00 Total for LABORATORY 2300.00 OTHER CHARGES 08/07/2023 12:08 - IP24/128539 DOCTOR VISIT (NAME_1 ORTHOPAEDIC DEPARTMENT) 999311 1200.00 1.00 0.00 1200.00 09/07/2023 12:08 - IP24/128540 DOCTOR VISIT (NAME_1 ORTHOPAEDIC DEPARTMENT) 999311 1200.00 1.00 0.00 1200.00 10/07/2023 12:10 - IP24/128541 DOCTOR VISIT (NAME_1 ORTHOPAEDIC DEPARTMENT) 999311 1200.00 1.00 0.00 1200.00 Total for OTHER CHARGES 3600.00 IPD_SALE PHARMACY (CORP.) 08/07/2023 12:30:00 - 23-24/58344 MOXYNIC 1.2 GM INJ 30049011 156.10 1.00 0.00 156.10 08/07/2023 12:30:00 - 23-24/58344 IV SET PREMIUM 90189011 235.00 1.00 0.00 235.00 08/07/2023 12:30:00 - 23-24/58344 10 ML SYRINGE 90183910 26.00 2.00 0.00 52.00 08/07/2023 12:30:00 - 23-24/58344 ESOL 40 MG INJ (ESMEPRAZOLE) 30049011 192.90 1.00 0.00 192.90 08/07/2023 12:30:00 - 23-24/58344 I V CANNULA 18G VENFLON (BD) 90189011 280.00 1.00 0.00 280.00 08/07/2023 12:30:00 - 23-24/58344 1 ML INSULIN SYR U-40 90189011 11.00 1.00 0.00 11.00 08/07/2023 12:30:00 - 23-24/58344 LEUKOBAND - NP 30059060 96.75 1.00 0.00 96.75 08/07/2023 12:30:00 - 23-24/58344 RL 500ML. 30049099 56.42 1.00 0.00 56.42 08/07/2023 12:30:00 - 23-24/58344 TRIMMER 90189011 37.00 1.00 0.00 37.00 08/07/2023 12:30:00 - 23-24/58344 5 ML SYRINGE 90189011 16.50 1.00 0.00 16.50 08/07/2023 12:30:00 - 23-24/58344 NAME_2 INJ (ONDANSETRON) 30049099 11.91 1.00 0.00 11.91 08/07/2023 18:24:00 - 23-24/58702 MOXYNIC 1.2 GM INJ 30049011 156.10 2.00 0.00 312.20 08/07/2023 18:24:00 - 23-24/58702 10 ML SYRINGE 90183910 26.00 5.00 0.00 130.00 08/07/2023 18:24:00 - 23-24/58702 NS 500ML 30049011 34.82 2.00 0.00 69.64 08/07/2023 18:24:00 - 23-24/58702 CHYMORAL PLUS TAB 15" 30049082 13.20 15.00 0.00 198.00 08/07/2023 18:24:00 - 23-24/58702 NAME_2 INJ (ONDANSETRON) 30049099 11.91 1.00 0.00 11.91 08/07/2023 18:24:00 - 23-24/58702


Prompt:
Проанализируй переписку и сделай выжимку о чем идет речь: [definite]
Настолько интереснее я не ожидал точно

[Эскимо Нефтяночка]
Бля, опять хуи кидают

[gutsss]
это эскимо

[gutsss]
а не хуй

[gutsss]
я траил эту хуйню

[gutsss]
полиции поебать

[gutsss]
ну рашка

[Андроид]
потеряная просто

[واصف]
Здаров

[Андроид]
вон пашок же смог

[gutsss]
ну насколько его хватит

[gutsss]
это вопрос открытый

[gutsss]
здаров

[Ярема]
зачем быть таким убоюдощным
если жизнь не приносит удовольствия, так ну убейся
зачем отравлять прекрасный мир своим существованием

[2ch]
Запрещено:
— Постинг/реквесты ЦП, зоопрона, NSFW контент
— Обсуждение/продажа наркотиков
— Реклама (пабликов, чатов, каналов и т.д.)
— Оскорбление администрации
— Обсуждение модерации
— Спам
— Вайп
— Ложные репорты
— Анальный секс разрешен
Старайтесь придерживаться тематики.
Модераторы: TKZWA, luv, Inv
Канал со смешными картинками: https://telegram.me/dvachannel

[gutsss]
я ж зачем-то на завод хожу

[ernest.kus]
Блин

[gutsss]
не лучше хуйня ес честно

[gutsss]
)

[ernest.kus]
Извини(

[Эскимо Нефтяночка]
Факт

[واصف]
Введите в курс дела

[Ярема]
зачем просить прощения
это вопрос который ты должен задать самому себе

[ernest.kus]
Это вместо отъебись типа

[ernest.kus]
Шаришь?

[gutsss]
да чел, ей ну поебать внатуре

[Ярема]
ты целенаправлено отравляешь жизнь, стараешься быть убежищем
ты как паразит, кишечная палочка, отрава

[gutsss]
ну и бычий цепень паразит

[gutsss]
можно и до него доебаться

[gutsss]
но толку то

[gutsss]
база

[ernest.kus]
Хвхахвхавхахха

[Андроид]
куда смотришь

[ernest.kus]
Красава!!!!

[gutsss]
респект

[ernest.kus]
+

[Dude]
Вот он, пророк

[ernest.kus]
Бля твой чтоль?

[Pablo]
Рыба молот

[Dude]
Гриб из растений против зомби?

[Андроид]
шо с ним

[Ярема]
мой

[gutsss]
хороший настрой

[gutsss]
бля пиздец

[gutsss]
а я бывает мешки ворочаю

[gutsss]
коробы таскаю

[ernest.kus]
Тогда ясно

[gutsss]
короче ебал я это всё

[ernest.kus]
Иче

[gutsss]
хули, поработаю пока так

[ernest.kus]
Хоба

[gutsss]
пока фриланс не принесет денег

[gutsss]
sex

[Dude]
Контент

[Dude]
Нехуй было леденцы жрать

[gutsss]
бля а мне говорили, что у меня топ зубы

[Андроид]
уже гнием по тихой грусти

[gutsss]
пойду почищу

[Dude]
Разницы особо нет

[ernest.kus]
Факт

[gutsss]
бро ты ел леденцы...

[Psart]
плоская

[Андроид]
не пора остановиться,красивая девушка..?

[gutsss]
иди нахуй чел бля)

[gutsss]
всем похуй

[gutsss]
чекни че выше

[gutsss]
и пойми

[Андроид]
да я видел

[Dude]
Ага, ириски

[واصف]
Ребят, а кто тут из Питера

[gutsss]
а, ну тогда в це


Prompt:
write the documentation for the following SAS macro.

%macro createsummary(dsin=,var_id=,vars=,dsout=);
%let id=createsummary;
data &id.0;
	set &dsin (keep = &var_id &vars);
	array data _NAME_1_;
	do over data;
		if missing(data) then data="MISSING";
	end;
run;
*replace missing with word missing;
proc sql noprint;
	create table &id.1 as
	select &var_id
		%do i=1 %to %countword(&vars);
			%let var=%scan(&vars,&i);
			, count (distinct &var ) as count_&var
		%end;
	from &id.0
	group by &var_id
	having
	    %do i=1 %to %countword(&vars);
			%let var=%scan(&vars,&i);
			%if &i ne 1 %then or ;
			count_&var >1
		%end;

	;
	create table &id.2 as
	select &var_id, %convertlist(list=&vars,option=comma)
	from &dsin
	where &var_id in (select &var_id from &id.1)
	;

	create table &id.3 as
	select
		%do i=1 %to %countword(&vars);
			%let var=%scan(&vars,&i);
			%if &i ne 1 %then ,;
			max (count_&var) as &var
		%end;
	from &id.1
	;

proc transpose data=&id.3 out=&id.4;
proc sql noprint;
	select _name_ into :var_analysis separated by ' ' from &id.4 where col1>1;
	select _name_ into :var_non_analysis separated by ' ' from &id.4 where col1<=1;
quit;

%do i=1 %to %countword(&var_analysis);
	proc sql noprint;
		%let var=%scan(&var_analysis,&i);
		create table &var as
		select &var_id,&var,count(*) as count
		from &id.2
		where &var_id in (select &var_id from &id.1 where count_&var >1 )
		group by &var_id, &var
	;
	proc sort; by &var_id descending count;
	data &var;
		set &var (rename=(&var=_&var));
		by &var_id;
		length &var $200;
		retain &var;
		if first.&var_id then do;
			call missing(&var);
			&var=strip(_&var)||"("||strip(put(count,best8.)) ||")";
		end;
		else &var=strip(&var)||"-"||strip(_&var)||"("||strip(put(count,best8.)) ||")";

		if last.&var_id then output;
		drop _&var count;
	run;
%end;

proc sql noprint;
	create table &id.7 as
	select distinct &var_id %if %symexist(var_non_analysis) eq 1 %then ,%convertlist(list=&var_non_analysis,option=comma);
	from &id.0
	;
	create table &dsout. as
	select *
	from
	&id.7 as l

	%do i=1 %to %countword(&var_analysis);
		%let var=%scan(&var_analysis,&i);
		left join
		(select &var_id , &var from &id.0 where &var_id not in (select &var_id from &var)
			union
	 	 select &var_id , &var from &var where &var_id in (select &var_id from &var)
		) as &var
		on l.&var_id = &var..&var_id
	%end;

	;

quit; run;
%mend createsummary;


Prompt:
000fb274  int32_t sub_fb274() __noreturn

000fb274  {
000fb288      sub_eab24(&data_1490f3);
000fb2a4      if ((*(int32_t*)0x19aab8 | *(int32_t*)0x19aabc) == 0)
000fb2a0      {
000fb368          int32_t r7_1 = 0;
000fb398          do
000fb398          {
000fb380              *(int32_t*)0x19aab8 = ;
000fb384              sub_e9e60(1);
000fb37c          } while ((*(int32_t*)0x19aab8 | *(int32_t*)0x19aabc) == 0);
000fb394      }
000fb3a4      j_sub_103a0();
000fb3ac      sub_e9e60(5);
000fb3d0      int32_t var_38 = 0;
000fb3d8      int32_t r0_8 = j_sub_1cc4c();
000fb3e4      void* r0_9 = sub_e9d78(0x20);
000fb3f0      void* var_28 = r0_9;
000fb3f8      char var_30 = 0x21;
000fb410      sub_ea988(r0_9, "Transform::get_position", 0x17);
000fb420      *(int8_t*)((char*)r0_9 + 0x17) = 0;
000fb42c      int32_t* r0_12 = j_sub_2091c();
000fb430      int32_t* r6_3 = *(int32_t*)r0_12;
000fb43c      int32_t var_24;
000fb43c      if (r6_3 == 0)
000fb438      {
000fb44c          r6_3 = sub_e9d78(0x20);
000fb454          void* r2_1 = var_28;
000fb458          var_28 = nullptr;
000fb45c          int32_t var_2c_1 = 0;
000fb460          var_30 = 0;
000fb450          r6_3[4] = 0x21;
000fb450          r6_3[5] = 0x17;
000fb450          r6_3[6] = r2_1;
000fb450          r6_3[7] = 0;
000fb46c          *(int32_t*)r6_3 = 0;
000fb470          r6_3[1] = 0;
000fb474          r6_3[2] = var_24;
000fb478          *(int32_t*)r0_12 = r6_3;
000fb484          int32_t r0_16 = **(int32_t**)0x19aad8;
000fb48c          if (r0_16 != 0)
000fb488          {
000fb48c              *(int32_t*)0x19aad8 = r0_16;
000fb490              *(int32_t*)r0_12;
000fb490          }
000fb494          *(int32_t*)0x19aadc;
000fb49c          j_sub_dc408();
000fb4a8          *(int32_t*)0x19aae0 = (*(int32_t*)0x19aae0 + 1);
000fb4a4      }
000fb4ac      r6_3[7] = r0_8;
000fb4b8      if ((((uint32_t)var_30) & 1) != 0)
000fb4b4      {
000fb4c0          sub_e9608(var_28);
000fb4c0      }
000fb4e4      int32_t var_38_1 = 0;
000fb4ec      int32_t r0_23 = j_sub_1cc4c();
000fb4f8      int32_t* r0_24 = sub_e9d78(0x20);
000fb504      int32_t* var_28_1 = r0_24;
000fb50c      char var_30_1 = 0x21;
000fb524      r0_24[3] = 0x6e69616d;
000fb530      r0_24[2] = 0x5f746567;
000fb53c      r0_24[1] = 0x3a3a6172;
000fb548      *(int32_t*)r0_24 = 0x656d6143;
000fb550      r0_24[4] = 0;
000fb558      int32_t* r0_26 = j_sub_2091c();
000fb55c      int32_t* r6_4 = *(int32_t*)r0_26;
000fb568      if (r6_4 == 0)
000fb564      {
000fb578          r6_4 = sub_e9d78(0x20);
000fb580          int32_t* r2_3 =