Naveen S navhits

## getMinutes.gs
var dueBy = "A"; // Provide the appropriate column ID
var todayIs = "B" // Provide the appropriate column ID
var resultField = "C"; // Provide the appropriate column ID
var onTrack = "D" // Provide the appropriate column ID

// first sheet
var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheets()[0];

// row range
var startRow = 2; // Mention the range start only

## HTML Redirect.html
<!doctype html>
<html>
  <head>
    <script>
      // Adding a Javascript redirct
      // The replace() will replace the current document with provided document
      window.location.replace('//example.com');
    </script>
    <noscript>
      <!--

## datetime_validation_with_pydantic_model.py
from pydantic import BaseModel
from datetime import datetime
import time

class Model(BaseModel):
    a: datetime
    b : datetime
    c : datetime
    d : datetime


## input_validation_with_pydantic_model.py
# This snippet reads a Pydantic model and gets data via std input and validates them back with the same model. This validated data can be exported for further processing in your own code.

from pydantic import BaseModel, AnyUrl
from typing import Optional

# Create a Pydantic Model. All Pydantic models needs be validated as dictionary mappings. A model can also contain a sub model. Such feature can be used to validate a Dictionary object within a dictionary. Pydantic also provides ways to validate data without creating models. For more info refer the docs, https://pydantic-docs.helpmanual.io/

class MyModel(BaseModel):
	name: str = None # Demonstrating the bahaviour of providing default value as None
	title: Optional[str] # Demonstrating the behavior when using Optional without setting a default value (will auto default to None)

## function_arguments_validation_with_pydantic_valdiator.py
from pydantic import validate_arguments, ValidationError

# A function with type hints
def arg_not_validated(s: str, i: int) -> str:
	return f'{type(s)}, {type(i)}'

# A function using the Pydantic validate_arugments decorator.
# Refer https://pydantic-docs.helpmanual.io/usage/validation_decorator/
@validate_arguments
def arg_validated(s: str, i: int) -> str:

## DriveQuery.py
import typing
import datetime
import warnings

from .enums import QueryFields, Operator, MimeType

class Query:

    def __init__(self) -> None:
        self.data = list()

## gitgemparse.py
def parse(path):
    with open(path) as gf:
        file_stream = gf.read()
    import re
    dependencies = []

    lines = re.findall(r"\s+?gem\s\s?(?:\'|\")[a-zA-Z0-9\_\-\.]+(?:\'|\"),?\s?(?:(?:\'|\")[0-9\.]+(?:\'|\"),\s)?git:.*", file_stream)
    if lines:
        temp = []
        for line in lines:

## fastapi_app.py
import os
import secrets

from fastapi import FastAPI, Security, HTTPException, status, Depends
from starlette.responses import JSONResponse
from fastapi.security.api_key import APIKeyHeader, APIKey


auth_header = APIKeyHeader(name="Authorization", auto_error=False)
	var dueBy = "A"; // Provide the appropriate column ID
	var todayIs = "B" // Provide the appropriate column ID
	var resultField = "C"; // Provide the appropriate column ID
	var onTrack = "D" // Provide the appropriate column ID

	// first sheet
	var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheets()[0];

	// row range
	var startRow = 2; // Mention the range start only
	<!doctype html>
	<html>
	<head>
	<script>
	// Adding a Javascript redirct
	// The replace() will replace the current document with provided document
	window.location.replace('//example.com');
	</script>
	<noscript>
	<!--
	from pydantic import BaseModel
	from datetime import datetime
	import time

	class Model(BaseModel):
	a: datetime
	b : datetime
	c : datetime
	d : datetime
	# This snippet reads a Pydantic model and gets data via std input and validates them back with the same model. This validated data can be exported for further processing in your own code.

	from pydantic import BaseModel, AnyUrl
	from typing import Optional

	# Create a Pydantic Model. All Pydantic models needs be validated as dictionary mappings. A model can also contain a sub model. Such feature can be used to validate a Dictionary object within a dictionary. Pydantic also provides ways to validate data without creating models. For more info refer the docs, https://pydantic-docs.helpmanual.io/

	class MyModel(BaseModel):
	name: str = None # Demonstrating the bahaviour of providing default value as None
	title: Optional[str] # Demonstrating the behavior when using Optional without setting a default value (will auto default to None)
	from pydantic import validate_arguments, ValidationError

	# A function with type hints
	def arg_not_validated(s: str, i: int) -> str:
	return f'{type(s)}, {type(i)}'

	# A function using the Pydantic validate_arugments decorator.
	# Refer https://pydantic-docs.helpmanual.io/usage/validation_decorator/
	@validate_arguments
	def arg_validated(s: str, i: int) -> str:
	import typing
	import datetime
	import warnings

	from .enums import QueryFields, Operator, MimeType

	class Query:

	def __init__(self) -> None:
	self.data = list()
	def parse(path):
	with open(path) as gf:
	file_stream = gf.read()
	import re
	dependencies = []

	lines = re.findall(r"\s+?gem\s\s?(?:\'\|\")[a-zA-Z0-9\_\-\.]+(?:\'\|\"),?\s?(?:(?:\'\|\")[0-9\.]+(?:\'\|\"),\s)?git:.*", file_stream)
	if lines:
	temp = []
	for line in lines:
	import os
	import secrets

	from fastapi import FastAPI, Security, HTTPException, status, Depends
	from starlette.responses import JSONResponse
	from fastapi.security.api_key import APIKeyHeader, APIKey


	auth_header = APIKeyHeader(name="Authorization", auto_error=False)