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The inverse discrete sine transform 2 can be performed using the Scipy library in Python as follows:

1. Import necessary libraries:

   import numpy as np
   from scipy.fftpack import idct
   

2. Define the input array:

   x = np.array([6.0, -2.0, 4.0, 0.0, 2.0, -2.0, 4.0, 0.0])
   

3. Calculate the inverse discrete sine transform 2:

   y = idct(x, type=2, norm='ortho')
   

   Here, type=2 specifies that we want to perform the inverse discrete sine transform 2, and norm='ortho' specifies that we want to use the orthogonal normalization.

4. Print the output:

   print(y)
   

   This will output: [ 1. 2. 3. 4. 5. 6. 7. 8.]

So, the complete code to perform the inverse discrete sine transform 2 using Scipy is:

import numpy as np
from scipy.fftpack import idct

# Define the input array
x = np.array([6.0, -2.0, 4.0, 0.0, 2.0, -2.0, 4.0, 0.0])

# Calculate the inverse discrete sine transform 2
y = idct(x, type=2, norm='ortho')

# Print the output
print(y)