#      
## Overview

**Fourier transform** is a method of transferring signals from the time domain into frequency. It is mostly used to identify the components of a signal. 


The `scipy.fft.fft2` function is provided by the Scientific Python (SciPy) library to perform Fourier transform on 2D signals.

## Syntax

The basic syntax of this function is as follows:

# Import fft package from scipy library
import scipy.fft
# Perform fft2 on the input signal to get 2D Fourier Transform
fft = scipy.fft.fft2(signal)

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## Explanation

The `scipy.fft.fft2` function performs Fast Fourier Transform (**FFT**), which is designed as a computationally efficient version of Fourier transform.  FFT improves speed by decreasing the number of calculations needed to analyze a waveform. 
The complete syntax of the `scipy.fft.fft2` function is:

scipy.fft.fft2(x, s=None, axes=(- 2, - 1), norm=None, 
               overwrite_x=False, workers=None, *, plan=None)

## Parameter values
* **`x`**: This is the input array that contains real or complex values of the signal.
* **`s`**: This is the sequence of ints that represents the shape of the output. The default value of this parameter is the shape of specified axes. This is an optional parameter value.
* **`axes`**: This is the sequence of integers that point to the axes over which to compute the FFT. The default axes are the last two. This is an optional parameter value.
* **`norm`**: Normalization to apply to the signal. {default = “backward”, “ortho”, “forward”}. This is an optional parameter value.
* **`overwrite_x`**: This is a boolean value that decides whether to perform FFT in-place or not. The default value of this parameter is `false`. This is an optional parameter value.
* **`workers`**: This is the integer value that represents the maximum number of workers allowed for parallel computing. This is an optional parameter value. 

## Return value
This function returns `out`, which is a complex array that contains the transformation of the input array around the specified axes.

## Example

An interesting example of this function is to compute the FFT of the 2D extension of a sinusoidal wave called sinusoidal grating. 

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


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What is the scipy.fft.fft2 function in Python?

The `scipy.fft.fft2` function performs a fast Fourier transform on 2D signals, efficiently transforming them from time to frequency domain.