Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

Sure! Here is the implementation of the Levenshtein distance function in Numba with the specified conditions: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i - 1] == t[j - 1] else 1
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost * 2)

    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4, 5])
t = np.array([1, 2, 4, 3, 5])
print(levenshtein(s, t))
This implementation of the Levenshtein distance function uses dynamic programming to compute the minimum number of operations (insertions, deletions, or swaps) required to transform one array into another. The function takes two numpy int arrays `s` and `t` as input and returns the Levenshtein distance between them. The cost of a character swap is 2. The function is compiled with Numba's `njit` decorator to run in nopython mode for improved performance.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i - 1] == t[j - 1] else 1
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost * 2)

    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4, 5])
t = np.array([1, 2, 4, 3, 5])
print(levenshtein(s, t))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i - 1] == t[j - 1] else 1
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost * 2)

    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4, 5])
t = np.array([1, 2, 4, 3, 5])
print(levenshtein(s, t))





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: 2 All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: False

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

Testing if the previous output contains the string from numba: True

Final Answer: True

Final Answer: True

Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

Here is the numba implementation of the Levenshtein distance algorithm taking two numpy int arrays: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Test the implementation
s = np.array([1, 2, 3, 4, 5], dtype=np.int64)
t = np.array([1, 2, 4, 3, 5], dtype=np.int64)

print(levenshtein(s, t))
This implementation of the Levenshtein distance algorithm in nopython mode uses dynamic programming to calculate the minimum number of edits required to transform one numpy int array into another. It also considers character swaps as a cost of 2.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Test the implementation
s = np.array([1, 2, 3, 4, 5], dtype=np.int64)
t = np.array([1, 2, 4, 3, 5], dtype=np.int64)

print(levenshtein(s, t))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Test the implementation
s = np.array([1, 2, 3, 4, 5], dtype=np.int64)
t = np.array([1, 2, 4, 3, 5], dtype=np.int64)

print(levenshtein(s, t))





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 39, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: False

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

Testing if the previous output contains the string from numba: True

Final Answer: True

Final Answer: False

Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

Here's the numba implementation of the Levenshtein distance with character swaps cost of 2: 

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import numba as nb
import numpy as np

@nb.njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
        
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Example usage
s1 = np.array([1, 2, 3, 4], dtype=np.int64)
s2 = np.array([1, 3, 2, 4], dtype=np.int64)

result = levenshtein(s1, s2)
print(result)
In this implementation, the `levenshtein` function calculates the Levenshtein distance between two input numpy arrays `s1` and `s2`. The function uses dynamic programming to efficiently compute the minimum number of operations (insertion, deletion, substitution, and character swap) required to transform `s1` into `s2`. The cost of a character swap is considered to be 2 in this implementation. The function is decorated with `@nb.njit` to run in nopython mode for better performance.

Extract Code

I extracted the following code from that output: 

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import numba as nb
import numpy as np

@nb.njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
        
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Example usage
s1 = np.array([1, 2, 3, 4], dtype=np.int64)
s2 = np.array([1, 3, 2, 4], dtype=np.int64)

result = levenshtein(s1, s2)
print(result)

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numba as nb
import numpy as np

@nb.njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)
    
    for i in range(m + 1):
        dp[i, 0] = i
        
    for j in range(n + 1):
        dp[0, j] = j
        
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i, j] = min(dp[i-1, j] + 1,         # deletion
                           dp[i, j-1] + 1,         # insertion
                           dp[i-1, j-1] + cost)    # substitution
            
            if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)  # character swap
    
    return dp[m, n]

# Example usage
s1 = np.array([1, 2, 3, 4], dtype=np.int64)
s2 = np.array([1, 3, 2, 4], dtype=np.int64)

result = levenshtein(s1, s2)
print(result)





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 42, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: False

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: True

Substring Evaluation

Testing if the previous output contains the string from numba: False

Final Answer: True

Final Answer: False

Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

Sure, here is the numba implementation of the Levenshtein distance algorithm with character swaps having a cost of 2: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m+1, n+1), dtype=np.int64)
    
    for i in range(m+1):
        dp[i, 0] = i
        
    for j in range(n+1):
        dp[0, j] = j
        
    for i in range(1, m+1):
        for j in range(1, n+1):
            cost = 0 if s1[i-1] == s2[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s1[i-1] == s2[j-2] and s1[i-2] == s2[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
s2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)
print(levenshtein(s1, s2))
This function calculates the Levenshtein distance between two numpy integer arrays `s1` and `s2` with character swaps having a cost of 2. The `njit` decorator is used to run the function in nopython mode for better performance.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m+1, n+1), dtype=np.int64)
    
    for i in range(m+1):
        dp[i, 0] = i
        
    for j in range(n+1):
        dp[0, j] = j
        
    for i in range(1, m+1):
        for j in range(1, n+1):
            cost = 0 if s1[i-1] == s2[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s1[i-1] == s2[j-2] and s1[i-2] == s2[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
s2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)
print(levenshtein(s1, s2))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s1, s2):
    m, n = len(s1), len(s2)
    dp = np.zeros((m+1, n+1), dtype=np.int64)
    
    for i in range(m+1):
        dp[i, 0] = i
        
    for j in range(n+1):
        dp[0, j] = j
        
    for i in range(1, m+1):
        for j in range(1, n+1):
            cost = 0 if s1[i-1] == s2[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s1[i-1] == s2[j-2] and s1[i-2] == s2[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
s2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)
print(levenshtein(s1, s2))





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 37, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: False

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

Testing if the previous output contains the string from numba: True

Final Answer: True

Final Answer: False

Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

Here is the implementation of Levenshtein distance in Numba: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    for i in range(m + 1):
        dp[i, 0] = i
    
    for j in range(n + 1):
        dp[0, j] = j
    
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4], dtype=np.int32)
t = np.array([1, 3, 2, 4], dtype=np.int32)

result = levenshtein(s, t)
print("Levenshtein distance:", result)
This code defines a function `levenshtein` that calculates the Levenshtein distance between two numpy int arrays `s` and `t`. The function is decorated with `@njit` to run in nopython mode for better performance. Character swaps have a cost of 2. The function initializes a 2D array `dp` to store the dynamic programming table and then fills it up according to the Levenshtein distance algorithm. Finally, it returns the Levenshtein distance between the two input arrays. You can test this function by providing two numpy int arrays as input and checking the calculated Levenshtein distance.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    for i in range(m + 1):
        dp[i, 0] = i
    
    for j in range(n + 1):
        dp[0, j] = j
    
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4], dtype=np.int32)
t = np.array([1, 3, 2, 4], dtype=np.int32)

result = levenshtein(s, t)
print("Levenshtein distance:", result)

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s, t):
    m, n = len(s), len(t)
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    for i in range(m + 1):
        dp[i, 0] = i
    
    for j in range(n + 1):
        dp[0, j] = j
    
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if s[i-1] == t[j-1] else 1
            dp[i, j] = min(dp[i-1, j] + 1, dp[i, j-1] + 1, dp[i-1, j-1] + cost)
            
            if i > 1 and j > 1 and s[i-1] == t[j-2] and s[i-2] == t[j-1]:
                dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]

# Test the function
s = np.array([1, 2, 3, 4], dtype=np.int32)
t = np.array([1, 3, 2, 4], dtype=np.int32)

result = levenshtein(s, t)
print("Levenshtein distance:", result)





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Levenshtein distance: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 39, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: False

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

Testing if the previous output contains the string from numba: True

Final Answer: True

Final Answer: False