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module "acm" {

  source = "terraform-aws-modules/acm/aws"

  domain_name = var.domain_name
  zone_id     = aws_route53_zone.this.zone_id

  wait_for_validation = true

  tags = {

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resource "aws_lb_target_group" "pypi_tg" {
  name     = "pypi-tg"
  port     = 8080
  protocol = "HTTP"
  vpc_id   = var.vpc_id
}

resource "aws_lb_target_group_attachment" "pypi_tg_attachment" {
  target_group_arn = aws_lb_target_group.pypi_tg[0].arn
  target_id        = aws_instance.pypi.id

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resource "aws_cloudwatch_event_rule" "cron_schedule" {
  name                = "es_backup_lambda_cron_schedule"
  description         = "Daily execution"
  schedule_expression = "rate(1 day)"
}

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import os
from datetime import datetime
from uuid import uuid4

import boto3
import requests
from requests_aws4auth import AWS4Auth


# configuration

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data "aws_iam_policy_document" "es_role_policy" {
  statement {
    actions = ["sts:AssumeRole"]

    principals {
      type        = "Service"
      identifiers = ["es.amazonaws.com"]
    }
  }
}

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module "lambda" {

  source = "terraform-aws-modules/lambda/aws"

  function_name = "lambda-es-backup"
  description   = "OpenSearch domain snapshot backup"
  handler       = "es_snapshot_handler.lambda_handler"
  runtime       = "python3.9"

  attach_policy = true

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resource "aws_iam_policy" "snapshot_passrole_policy" {
  name        = "snapshot_passrole_policy"
  path        = "/"
  description = "Allow role to assume the snapshot role to create OpenSearch snapshots"

  policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {

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from torch import nn

class NNApproximator(nn.Module):
    def __init__(
        self,
        num_inputs: int = 1,
        num_outputs: int = 1,
        num_hidden: int = 1,
        dim_hidden: int = 1,
        act: nn.Module = nn.Tanh(),

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import torch

def f(nn: NNApproximator, x: torch.Tensor) -> torch.Tensor:
    """Compute the value of the approximate solution from the NN model"""
    return nn(x)


def df(nn: NNApproximator, x: torch.Tensor = None, order: int = 1) -> torch.Tensor:
    """Compute neural network derivative with respect to the input feature(s) using PyTorch autograd engine"""
    df_value = f(nn, x)

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T_0 = 0
F_0 = 1

boundary = torch.Tensor([T_0])
boundary.requires_grad = True

boundary_loss = f(nn, boundary) - F0