Adityanagraj

2-6
import numpy as np

x=list(map(int,input().split(',')))
y=list(map(int,input().split(',')))
data1 = x#[3, 5, 5, 5, 8, 11, 11, 11, 13]
data2 = y#[ 3, 5, 5, 5, 8, 11, 11, 11, 20 ] 

mean1 = np.mean(data1)
mean2 = np.mean(data2)

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from diagrams import Cluster,Diagram
from diagrams.gcp.network import Armor as AR
from diagrams.gcp.network import LoadBalancing as LB
from diagrams.gcp.compute import ComputeEngine as CE
from diagrams.gcp.database import SQL as SQL
from diagrams.gcp.compute import KubernetesEngine as GKE
from diagrams.k8s.compute import Pod as Pod
from diagrams.gcp.storage import Storage as GCS
from diagrams.gcp.network import VirtualPrivateCloud as VPC
        

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gitlabUrl: <your gitlab URL>
runnerRegistrationToken: <your runner secret>
rbac:
  create: True
  clusterWideAccess: true
# runners:
#   image: ubuntu:18.04
#   privileged: true
runners:
  privileged: true

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stages:
  - test

publish:
  image: docker:20.10.12-git
  stage: test
  services:
    - docker:20.10.12-dind
  tags: 
    - docker

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%-------------------------
% Resume in Latex
% Author : Sourabh Bajaj
% License : MIT
%------------------------

\documentclass[letterpaper,11pt]{article}

\usepackage{latexsym}
\usepackage[empty]{fullpage}

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input, target = val_ds[10]

predict_single(input, target, model)

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def predict_single(input, target, model):
    inputs = input.unsqueeze(0)
    predictions = model(inputs)               # fill this #model(inputs)
    prediction = predictions[0].detach()
    print("Input:", input)
    print("Target:", target)
    print("Prediction:", prediction)

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model = FishModel()
epochs = 1000
lr = 1e-2
history1 = fit(epochs, lr, model, train_loader, val_loader)

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def evaluate(model, val_loader):
    outputs = [model.validation_step(batch) for batch in val_loader]
    return model.validation_epoch_end(outputs)

def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
    history = []
    optimizer = opt_func(model.parameters(), lr)
    for epoch in range(epochs):
        # Training Phase 
        for batch in train_loader:

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model = FishModel()