I got an error in Pycham after trying to run a test code for deep learning - deep-learning

Question

# USAGE
# python train_simple_nn.py --dataset animals --model output/simple_nn.model --label-bin output/simple_nn_lb.pickle --plot output/simple_nn_plot.png
# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")
# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import random
import pickle
import cv2
import os
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset of images")
ap.add_argument("-m", "--model", required=True, help="path to output trained model")
ap.add_argument("-l", "--label-bin", required=True, help="path to output label binarizer")
ap.add_argument("-p", "--plot", required=True, help="path to output accuracy/loss plot")
args = vars(ap.parse_args())
# initialize the data and labels
print("[INFO] loading images...")
data = []
labels = []
# grab the image paths and randomly shuffle them
imagePaths = sorted(list(paths.list_images(args["dataset"])))
random.seed(42)
random.shuffle(imagePaths)
# loop over the input images
for imagePath in imagePaths:
# load the image, resize the image to be 32x32 pixels (ignoring
# aspect ratio), flatten the image into 32x32x3=3072 pixel image
# into a list, and store the image in the data list
image = cv2.imread(imagePath)
image = cv2.resize(image, (32, 32)).flatten()
data.append(image)
# extract the class label from the image path and update the
# labels list
label = imagePath.split(os.path.sep)[-2]
labels.append(label)
# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
I found a test code for studing Deep learning.
And tried to run in Pycharm. but I got this error message.
Actually I couldn't understand what that parser function is doing here.
could you explain about that code and about the error?
---error i got in Pycharm -----------------------
C:\Users\giyeo\anaconda3\envs\tf\python.exe "D:/GiyeonLee/09. Machine Learning/Pycharm/Tutorial/keras-tutorial/train_simple_nn.py"
2020-07-06 13:56:28.409237: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
usage: train_simple_nn.py [-h] -d DATASET -m MODEL -l LABEL_BIN -p PLOT
train_simple_nn.py: error: the following arguments are required: -d/--dataset, -m/--model, -l/--label-bin, -p/--plot
Process finished with exit code 2
Thanks for reading my quation..