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{
  "dependencies": {
    "@types/jest": "^27.0.3",
    "@types/node": "^17.0.0",
    "@types/react": "^17.0.37",
    "@types/react-dom": "^17.0.11",
    "typescript": "^4.5.4",
    ...
  },
  "scripts": {

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# This workflow will build 'client' folder, test and deploy to github pages on branch gh-pages
# For more information see:
#   - https://help.github.com/actions/language-and-framework-guides/using-nodejs-with-github-actions
#   - https://github.com/marketplace/actions/deploy-to-github-pages

name: Build and Deploy Frontend

on:
  push:
    branches: [ main ]

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# Version: 2.4.1
import tensorflow as tf


class MaliciousModule(tf.keras.Model):
    def __init__(self):
        super(MaliciousModule, self).__init__()
        self.dense = tf.keras.layers.Dense(1, activation='sigmoid')

    @tf.function

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def combined_loss(gan_model, **kwargs):
    """Wrapper function for combine adversarial loss, use as generator loss"""
    # Define non-adversarial loss - for example L1
    non_adversarial_loss = tf.losses.absolute_difference(
        gan_model.real_data, gan_model.generated_data)
    
    # Define generator loss
    generator_loss = tf.contrib.gan.losses.least_squares_generator_loss(
        gan_model,
        **kwargs)

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import tensorflow as tf

class ImportGraph():
    """  Importing and running isolated TF graph """
    def __init__(self, loc):
        # Create local graph and use it in the session
        self.graph = tf.Graph()
        self.sess = tf.Session(graph=self.graph)
        with self.graph.as_default():
            # Import saved model from location 'loc' into local graph

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import tensorflow as tf

class Graph():
    """  Importing and running isolated TF graph """
    def __init__(self, loc):
        # Create local graph and use it in the session
        self.graph = tf.Graph()
        self.sess = tf.Session(graph=self.graph)
        with self.graph.as_default():
            # Import saved model from location 'loc' into local graph

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sess = tf.Session()

# Import graph from the path and recover session
saver = tf.train.import_meta_graph('models/model_name.meta', clear_devices=True)
saver.restore(sess, 'models/model_name')

# There are TWO options how to access the operation (choose one)
  # FROM SAVED COLLECTION:
activation = tf.get_collection('activation')[0]
  # BY NAME:

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import tensorflow as tf
### Linear Regression ###
# Input placeholders
x = tf.placeholder(tf.float32, name='x')
y = tf.placeholder(tf.float32, name='y')
# Model parameters
W1 = tf.Variable([0.1], tf.float32)
W2 = tf.Variable([0.1], tf.float32)
W3 = tf.Variable([0.1], tf.float32)
b = tf.Variable([0.1], tf.float32)

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#!/bin/bash
# Update
apt-get update && apt-get upgrade -y
# Install python+packages and curl
apt-get install -y python3 python3-pip python3-numpy python3-scipy python3-matplotlib ipython3 ipython3-notebook python3-pandas python3-nose curl wget
# Update pip
python3 -m easy_install -U pip

# Correctinng python file 
# which causes stuck during package installation