python please, need the "your code here" for the searches

from queue import PriorityQueue

from pprint import pprint

# Graph of cities with connections to each city. Similar to our class exercises, you can draw it on a piece of paper

# with step$-$by$-$step node inspection for better understanding

graph $=\{$

'San Bernardino': $[\text{'}$Riverside$\text{'},$ 'Rancho Cucamonga'$],$

'Riverside': $[\text{'}$San Bernardino', 'Ontario', 'Pomona'$],$

'Rancho Cucamonga': $[\text{'}$San Bernardino', 'Azusa', 'Los Angeles'$],$

'Ontario': $[\text{'}$Riverside$\text{'},$ 'Whittier', 'Los Angeles'$],$

'Pomona': $[\text{'}$Riverside$\text{'},$ 'Whittier', 'Azusa', 'Los Angeles'$],$

'Whittier': $[\text{'}$Ontario$\text{'},$'Pomona', 'Los Angeles'$],$

'Azusa': $[\text{'}$Rancho Cucamonga', 'Pomona', 'Arcadia'$],$

'Arcadia': $[\text{'}$Azusa$\text{'},$ 'Los Angeles'$],$

'Los Angeles': $[\text{'}$Rancho Cucamonga', 'Ontario', 'Pomona', 'Whittier', 'Arcadia'$]$

$\}$

# Weights are treated as g$($n$)$ function as we studied in our class lecture which represents the backward cost.

# In the data structure below, the key represents the cost from a source to target node. For example, the first

# entry shows that there is a cost of $2$ for going from San Bernardino to Riverside.

weights $=\{$

$(\text{'}$San Bernardino', 'Riverside'$)$: $2,$

$(\text{'}$San Bernardino', 'Rancho Cucamonga'$)$: $1,$

$(\text{'}$Riverside$\text{'},$ 'Ontario'$)$: $1,$

$(\text{'}$Riverside$\text{'},$ 'Pomona'$)$: $3,$

$(\text{'}$Rancho Cucamonga', 'Los Angeles'$)$: $5,$

$(\text{'}$Pomona$\text{'},$ 'Los Angeles'$)$: $2,$

$(\text{'}$Ontario$\text{'},$ 'Whittier'$)$: $2,$

$(\text{'}$Ontario$\text{'},$ 'Los Angeles'$)$: $3,$

$(\text{'}$Rancho Cucamonga', 'Azusa'$)$: $3,$

$(\text{'}$Pomona$\text{'},$ 'Azusa'$)$: $2,$

$(\text{'}$Pomona$\text{'},$ 'Whittier'$)$: $2,$

$(\text{'}$Azusa$\text{'},$ 'Arcadia'$)$: $1,$

$(\text{'}$Whittier$\text{'},$ 'Los Angeles'$)$: $2,$

$(\text{'}$Arcadia$\text{'},$ 'Los Angeles'$)$: $2$

$\}$

# heurist is the h$($n$)$ function as we studied in our class lecture which represents the forward cost.

# In the data structure below, each entry represents the h$($n$)$ value. For example, the second entry

# shows that h$($Riverside$)$ is $2($i$.$e$.,$ h value as forward cost for eaching at Riverside assuming that

# your current$/$start city is San Bernardino$)$

heuristic $=\{$

'San Bernardino': $4,$

'Riverside': $2,$

'Rancho Cucamonga': $1,$

'Ontario': $1,$

'Pomona': $3,$

'Whittier': $4,$

'Azusa': $3,$

'Arcadia': $2,$

'Los Angeles': $0$

$\}$

# Data structure to implement search algorithms. Each function below currently has one line of code

# returning empty solution with empty expanded cities. You can remove the current return statement and

# implement your code to complete the functions.