Please write Python code in an original and efficient manner. It should be both original and capable of working efficiently.

import numpy

import pandas

class InputError$($Exception$)$:

pass

def fill$\_$spaces$($data: numpy.array, editing$\_$mode: int$)->$ numpy.array:

$"""$ This function will take a numpy.array of size $($m$,)$ where m $>0$ as input.

It is known that the valid numbers within this array are between $0$ and $100.$

It will fill all values that do not comply with the specified range;

with the previous valid value if editing$\_$mode is $0,$

with the average value of the previous and next valid values if editing$\_$mode is $1,$

with the average value of the remaining values if editing$\_$mode is $2.$

If all data values are outside the desired range, False will be returned.

Args:

data: numpy.array

Returns:

result: numpy.array or bool $($False$)$

Example:

data: numpy.array$([1,2,3,4,5,6,-1,8,9,10,11])$

editing$\_$mode: $0$

result: numpy.array$([1,2,3,4,5,6,6,8,9,10,11])$

editing$\_$mode: $1$

result: numpy.array$([1,2,3,4,5,6,7,8,9,10,11])$

editing$\_$mode: $2$

result: numpy.array$([1,2,3,4,5,6,5\mathrm{.}9,8,9,10,11])$

$"""$

return NotImplementedError

def remove$\_$duplicates$($df: panclass InputError$($Exception$)$:

pass

def fill$\_$spaces$($data: numpy.array, editing$\_$mode: int$)->$ numpy.array:

$"""$ This function will take a numpy.array of size $,)$ where $m>0$ as input

It is known that the valid numbers within this array are between $0$ and

It will fill all values that do not comply with the specified range;

with the previous valid value if editing$\_$mode is $0,$

with the average value of the previous and next valid values if editing

with the average value of the remaining vadas.DataFrame$)->$ pandas.DataFrame:

$"""$ This function removes rows of duplicate timestamps in a DataFrame with timestamps in its first column

$($column name is not known$)$ and keeps only the row of the most recent$/$newest timestamp in the DataFrame.

The input DataFrame remains unchanged when this function is executed.

Args:

df: DataFrame $($pandas$.$DataFrame$)$

Returns:

result: DataFrame $($pandas$.$DataFrame$)$

Example:

Shown in the visual.

$"""$

return NotImplementedError

def days$\_$until$\_$end$\_$of$\_$year$($input$\_$date: str$)->$ int:

$"""$ This function returns the number of days left until the end of the year when a date in the format of $"$YYYY$-$MM$-$DD$"$ is received.

The current day is not counted.

Args:

input$\_$date: str

Returns:

result: int

Example:

input$\_$date $="2022-12-29"$

result $=2$

$"""$

return NotImplementedError

def my$\_$max$\_$pooling$($matrix: numpy.array$)->$ numpy.array:

$"""$ When a square matrix of size $($N$,$ N$)$ with N being an even number is received as input,

this function creates a new square matrix of size $($N$/2,$ N$/2)$ using the maximum number

in each $(2,2)$ non$-$overlapping area.

Args:

matrix $($numpy$.$array$)$: Input array of size $($N$,$N$).$ N is a multiple of $4.$

Returns:

result: numpy.array

Example:

Shown in the visual.

$"""$

return NotImplementedError

def find$\_$problematic$\_$boroughs$($df: pandas.DataFrame$)->$ list:

$"""$ This function returns the names of the top $5$ boroughs with the highest number of illegal parking complaints

$($Illegal Parking $/$ Complaints$)$ and their complaint counts as tuples from the DataFrame

$"311-$service$-$requests.csv$"$ read with the file$\_$read function.

Args:

df: Dataframe $($pandas$.$DataFrame$)$

Returns:

result: list of tuples

Example:

$[("$BROOKLYN$",1147),..]$

$"""$

return NotImplementedError

def unique$\_$agency$\_$count$($df: pandas.DataFrame$)->$ int:

$"""$ This function returns the count of unique agency names in the DataFrame

$"311-$service$-$requests.csv$"$ read with the file$\_$read function.

Args:

df: Dataframe $($pandas$.$DataFrame$)$

Returns:

result: int

$"""$

return NotImplementedError

def most$\_$common$\_$complaint$\_$types$($df: pandas.DataFrame, N: int$)->$ list:

$"""$ This function returns a string list of the top N most common complaint types

from the DataFrame $"311-$service$-$requests.csv$"$ read with the file$\_$read function.

N can be between $1$ and $165.$

Args:

df: DataFrame to process $($pandas$.$DataFrame$)$

N: Number of Repetitions $($int$)$

Returns:

result: list

$"""$

return NotImplementedError

def file$\_$read$($file$\_$path: str$)->$ pandas.DataFrame:

$"""$ This function reads $.$csv or $.$tab files and returns the output as a Pandas DataFrame.

It is certain that the first row of these files will contain attribute names.

Args:

file$\_$path $($str$)$: File path to be read with $.$csv or $.$tab extension

Returns:

result: pandas.DataFrame

$"""$

return NotImplementedError