eu criei esse esqueleto IA

import tensorflow as tf
from tensorflow import keras
import numpy as np
import requests
import json

# Define constants
X_INPUT_SIZE = 10
Y_OUTPUT_SIZE = 1

# Collect data from Azure API
def collect_data(api_key):
    headers = {'api-version': '2021-04-01', 'Ocp-Apim-Subscription-Key': api_key}
    response = requests.get('https://management.azure.com/subscriptions/{subscriptionid}/resourcegroups?api-version=2021-04-01', headers=headers)
    data = response.json()
    return data

# Preprocess the collected data to prepare it for training the model
def preprocess_data(data):
    # ...
    return preprocessed_data

# Train the model
def train_model(X_train, y_train):
    # Define the model architecture
    model = keras.Sequential([
        keras.layers.Dense(64, activation='relu', input_shape=(X_INPUT_SIZE,)),
        keras.layers.Dense(32, activation='relu'),
        keras.layers.Dense(Y_OUTPUT_SIZE, activation='linear')
    ])
    
    # Compile the model
    model.compile(loss='mean_squared_error', optimizer='adam')
    
    # Train the model
    model.fit(X_train, y_train, epochs=10)
    
    # Save the model
    model.save('model.h5')
    print('Model trained and saved successfully.')

# Use the model to solve problems in the Azure environment
def solve_problem(input_data):
    # Load the model
    model = keras.models.load_model('model.h5')
    
    # Preprocess the input data
    preprocessed_input = preprocess_data(input_data)
    
    # Make a prediction using the model
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