My code is not working it's throwing error

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from numpy import expand$\_$dims

from numpy import zeros

from numpy import ones

from numpy import vstack

from numpy.random import randn

from numpy.random import randint

from keras.optimizers import Adam

from keras.models import Sequential

from keras.layers import Dense,Reshape,Flatten,Conv$2$D$,$Conv$2$DTranspose,LeakyReLU,Dropout

from matplotlib import pyplot

import numpy as np

import cv$2$

# function to generate discriminator model

def define$\_$discriminator$($in$\_$shape$=(32,32,3))$:

model $=$ Sequential$()$

# normal

model.add$($Conv$2$D$(64,(3,3),$ padding$=$'same', input$\_$shape$=$in$\_$shape$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# downsample

model.add$($Conv$2$D$(128,(3,3),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# downsample

model.add$($Conv$2$D$(128,(3,3),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# downsample

model.add$($Conv$2$D$(256,(3,3),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# classifier

model.add$($Flatten$())$

model.add$($Dropout$(0\mathrm{.}4))$

model.add$($Dense$(1,$ activation$=$'sigmoid'$))$

# compile model

opt $=$ Adam$($lr$=0\mathrm{.}0002,$ beta$\_1=0\mathrm{.}5)$

model.compile$($loss$=$'binary$\_$crossentropy', optimizer$=$opt, metrics$=[\text{'}$accuracy$\text{'}])$

return model

# function to generate standalone generator model

def define$\_$generator$($latent$\_$dim$)$:

model $=$ Sequential$()$

# foundation for $4$x$4$ image

n$\_$nodes $=256*4*4$

model.add$($Dense$($n$\_$nodes, input$\_$dim$=$latent$\_$dim$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

model.add$($Reshape$((4,4,256)))$

# upsample to $8$x$8$

model.add$($Conv$2$DTranspose$(128,(4,4),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# upsample to $16$x$16$

model.add$($Conv$2$DTranspose$(128,(4,4),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# upsample to $32$x$32$

model.add$($Conv$2$DTranspose$(128,(4,4),$ strides$=(2,2),$ padding$=$'same'$))$

model.add$($LeakyReLU$($alpha$=0\mathrm{.}2))$

# output layer

model.add$($Conv$2$D$(3,(3,3),$ activation$=$'tanh', padding$=$'same'$))$

return model

# define the combined generator and discriminator model, for updating the generator

def define$\_$gan$($g$\_$model, d$\_$model$)$:

# make weights in the discriminator not trainable

d$\_$model.trainable $=$ False

# connect them

model $=$ Sequential$()$

# add generator

model.add$($g$\_$model$)$

# add the discriminator

model.add$($d$\_$model$)$

# compile model

opt $=$ Adam$($lr$=0\mathrm{.}0002,$ beta$\_1=0\mathrm{.}5)$

model.compile$($loss$=$'binary$\_$crossentropy', optimizer$=$opt$)$

return model

# load and prepare cifar$10$ training images

def load$\_$real$\_$samples$()$:

# load cifar$10$ dataset

X $=$ np$.$load$(\text{'}$img$\_$data.txt$.$npy$\text{'})$

# convert from unsigned ints to floats

#X $=$ trainX.astype$(\text{'}$float$32\text{'})$

# scale from $[0,255]$ to $[-1,1]$

#X $=($X $-127\mathrm{.}5)/127\mathrm{.}5$

return X

# select real samples

def generate$\_$real$\_$samples$($dataset$,$ n$\_$samples$)$:

# choose random instances

ix $=$ randint$(0,$ dataset.shape$[0],$ n$\_$samples$)$

# retrieve selected images

X $=$ dataset$[$ix$]$

# generate 'real' class labels $(1)$

y $=$ ones$(($n$\_$samples, $1))$

return X$,$ y

# generate points in latent space as input for the generator

def generate$\_$latent$\_$points$($latent$\_$dim, n$\_$samples$)$:

# generate points in the latent space

x$\_$input $=$ randn$($latent$\_$dim $*$ n$\_$samples$)$

# reshape into a batch of inputs for the network

x$\_$input $=$ x$\_$input.reshape$($n$\_$samples, latent$\_$dim$)$

return x$\_$input

# use the generator to generate n fake examples, with class labels

def generate$\_$fake$\_$samples$($g$\_$model, latent$\_$dim, n$\_$samples$)$:

# generate points in latent space

x$\_$input $=$ generate$\_$latent$\_$points$($latent$\_$dim, n$\_$samples$)$

# predict outputs

X $=$ g$\_$model.predict$($x$\_$input$)$

# create 'fake' class labels $(0)$

y $=$ zeros$(($n$\_$samples, $1))$

return X$,$ y

# create and save a plot of generated images

def save$\_$plot$($examples$,$ epoch, n$=7)$:

# scale from $[-1,1]$ to $[0,1]$

examples $=($examples $+1)/2\mathrm{.}0$

# plot images

for i in range$($n $*$ n$)$:

# define subplot

pyplot.subplot$($n$,$ n$,1+$ i$)$

# turn off axis

pyplot.axis$(\text{'}$off$\text{'})$

# plot raw pixel data

pyplot.imshow$($examples$[$i$])$

cv$2.$imwrite$("$img$/"+$str$($i$)+".$jpg$",$examples$[$i$])$

# save plot to file

filename $=$ 'model$/$generated$\_$plot$\_$e$\%03$d$.$png$\text{'}\%($epoch$+1)$

pyplot.savefig$($filename$)$

pyplot.close$()$

# evaluate the discriminator, plot generated images, save generator model

def summarize$\_$performance$($epoch$,$ g$\_$model, d$\_$model, dataset, latent$\_$dim, n$\_$samples$=150)$:

# prepare real samples

X$\_$real, y$\_$real $=$ generate$\_$real$\_$samples$($dataset$,$ n$\_$samples$)$

# evaluate discriminator on real examples

$\_,$ acc$\_$real $=$ d$\_$model.evaluate$($X$\_$real, y$\_$real, verbose$=0)$

# prepare fake examples

x$\_$fake, y$\_$fake $=$ generate$\_$fake$\_$samples$($g$\_$model, latent$\_$dim, n$\_$samples$)$

# evaluate discriminator on fake examples

$\_,$ acc$\_$fake $=$ d$\_$model.evaluate$($x$\_$fake, y$\_$fake, verbose$=0)$

# summarize discriminator performance

print$(\text{'}>$Accuracy real: $\%\mathrm{.}0$f$\%\%,$ fake: $\%\mathrm{.}0$f$\%\%\text{'}\%($acc$\_$real$*100,$ acc$\_$fake$*100))$

# save plot

save$\_$plot$($x$\_$fake, epoch$)$

# save the generator model tile file

filename $=$ 'model$/$generator$\_$model$\_\%03$d$.$h$5\text{'}\%($epoch$+1)$

g$\_$model.save$($filename$)$

train$($g$\_$model, d$\_$model, gan$\_$model, dataset, latent$\_$dim$)$

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please help and help cloudeploy