Initial Query

Disassemble the following python into a function called foo: 2 0 LOAD_CONST 1 (16) 2 STORE_FAST 2 (PH) 4 4 LOAD_CONST 2 (0) 6 STORE_FAST 3 (extra) 5 8 LOAD_CONST 2 (0) 10 STORE_FAST 4 (col) 6 12 BUILD_LIST 0 14 STORE_FAST 5 (pivots) 8 16 LOAD_CONST 3 (<code object <listcomp> at 0x101093660, file "/private/tmp/a.py", line 8>) 18 LOAD_CONST 4 ('rref.<locals>.<listcomp>') 20 MAKE_FUNCTION 0 22 LOAD_GLOBAL 0 (range) 24 LOAD_FAST 0 (matrix) 26 LOAD_ATTR 1 (shape) 28 LOAD_CONST 2 (0) 30 BINARY_SUBSCR 32 CALL_FUNCTION 1 34 GET_ITER 36 CALL_FUNCTION 1 38 STORE_FAST 6 (used_for_row) 10 40 LOAD_FAST 0 (matrix) 42 LOAD_FAST 2 (PH) 44 BINARY_MODULO 46 STORE_FAST 0 (matrix) 11 >> 48 LOAD_FAST 4 (col) 50 LOAD_FAST 3 (extra) 52 BINARY_ADD 54 LOAD_FAST 0 (matrix) 56 LOAD_ATTR 1 (shape) 58 LOAD_CONST 5 (1) 60 BINARY_SUBSCR 62 LOAD_CONST 5 (1) 64 BINARY_SUBTRACT 66 COMPARE_OP 0 (<) 68 EXTENDED_ARG 2 70 POP_JUMP_IF_FALSE 628 72 LOAD_FAST 4 (col) 74 LOAD_FAST 0 (matrix) 76 LOAD_ATTR 1 (shape) 78 LOAD_CONST 2 (0) 80 BINARY_SUBSCR 82 COMPARE_OP 0 (<) 84 EXTENDED_ARG 2 86 POP_JUMP_IF_FALSE 628 13 88 LOAD_FAST 0 (matrix) 90 LOAD_FAST 4 (col) 92 LOAD_FAST 4 (col) 94 LOAD_FAST 3 (extra) 96 BINARY_ADD 98 BUILD_TUPLE 2 100 BINARY_SUBSCR 102 LOAD_CONST 2 (0) 104 COMPARE_OP 2 (==) 106 EXTENDED_ARG 1 108 POP_JUMP_IF_FALSE 262 14 110 LOAD_GLOBAL 2 (np) 112 LOAD_METHOD 3 (all) 114 LOAD_FAST 0 (matrix) 116 LOAD_CONST 0 (None) 118 LOAD_CONST 0 (None) 120 BUILD_SLICE 2 122 LOAD_FAST 4 (col) 124 BUILD_TUPLE 2 126 BINARY_SUBSCR 128 LOAD_CONST 2 (0) 130 COMPARE_OP 2 (==) 132 CALL_METHOD 1 134 POP_JUMP_IF_FALSE 146 15 136 LOAD_FAST 3 (extra) 138 LOAD_CONST 5 (1) 140 INPLACE_ADD 142 STORE_FAST 3 (extra) 16 144 JUMP_ABSOLUTE 48 17 >> 146 LOAD_GLOBAL 2 (np) 148 LOAD_METHOD 4 (argwhere) 150 LOAD_FAST 0 (matrix) 152 LOAD_CONST 0 (None) 154 LOAD_CONST 0 (None) 156 BUILD_SLICE 2 158 LOAD_FAST 4 (col) 160 LOAD_FAST 3 (extra) 162 BINARY_ADD 164 BUILD_TUPLE 2 166 BINARY_SUBSCR 168 LOAD_CONST 2 (0) 170 COMPARE_OP 3 (!=) 172 CALL_METHOD 1 174 LOAD_METHOD 5 (flatten) 176 CALL_METHOD 0 178 LOAD_CONST 6 (-1) 180 BINARY_SUBSCR 182 STORE_FAST 7 (other) 18 184 LOAD_FAST 7 (other) 186 LOAD_FAST 4 (col) 188 COMPARE_OP 0 (<) 190 POP_JUMP_IF_FALSE 202 19 192 LOAD_FAST 3 (extra) 194 LOAD_CONST 5 (1) 196 INPLACE_ADD 198 STORE_FAST 3 (extra) 20 200 JUMP_ABSOLUTE 48 22 >> 202 LOAD_GLOBAL 6 (list) 204 LOAD_FAST 0 (matrix) 206 LOAD_FAST 7 (other) 208 BINARY_SUBSCR 210 CALL_FUNCTION 1 212 LOAD_GLOBAL 6 (list) 214 LOAD_FAST 0 (matrix) 216 LOAD_FAST 4 (col) 218 BINARY_SUBSCR 220 CALL_FUNCTION 1 222 ROT_TWO 224 LOAD_FAST 0 (matrix) 226 LOAD_FAST 4 (col) 228 STORE_SUBSCR 230 LOAD_FAST 0 (matrix) 232 LOAD_FAST 7 (other) 234 STORE_SUBSCR 23 236 LOAD_FAST 6 (used_for_row) 238 LOAD_FAST 7 (other) 240 BINARY_SUBSCR 242 LOAD_FAST 6 (used_for_row) 244 LOAD_FAST 4 (col) 246 BINARY_SUBSCR 248 ROT_TWO 250 LOAD_FAST 6 (used_for_row) 252 LOAD_FAST 4 (col) 254 STORE_SUBSCR 256 LOAD_FAST 6 (used_for_row) 258 LOAD_FAST 7 (other) 260 STORE_SUBSCR 25 >> 262 LOAD_FAST 5 (pivots) 264 LOAD_METHOD 7 (append) 266 LOAD_FAST 4 (col) 268 LOAD_FAST 3 (extra) 270 BINARY_ADD 272 CALL_METHOD 1 274 POP_TOP 26 276 LOAD_FAST 0 (matrix) 278 LOAD_FAST 4 (col) 280 LOAD_FAST 4 (col) 282 LOAD_FAST 3 (extra) 284 BINARY_ADD 286 BUILD_TUPLE 2 288 BINARY_SUBSCR 290 STORE_FAST 8 (pivot) 27 292 LOAD_FAST 4 (col) 294 LOAD_FAST 3 (extra) 296 BINARY_ADD 298 LOAD_FAST 1 (graphlen) 300 COMPARE_OP 0 (<) 302 EXTENDED_ARG 1 304 POP_JUMP_IF_FALSE 348 28 306 LOAD_GLOBAL 2 (np) 308 LOAD_METHOD 8 (abs) 310 LOAD_FAST 8 (pivot) 312 CALL_METHOD 1 314 LOAD_CONST 5 (1) 316 COMPARE_OP 2 (==) 318 EXTENDED_ARG 1 320 POP_JUMP_IF_TRUE 396 322 LOAD_GLOBAL 2 (np) 324 LOAD_METHOD 8 (abs) 326 LOAD_FAST 8 (pivot) 328 CALL_METHOD 1 330 LOAD_FAST 2 (PH) 332 LOAD_CONST 5 (1) 334 BINARY_SUBTRACT 336 COMPARE_OP 2 (==) 338 EXTENDED_ARG 1 340 POP_JUMP_IF_TRUE 396 342 LOAD_ASSERTION_ERROR 344 RAISE_VARARGS 1 346 JUMP_FORWARD 48 (to 396) 30 >> 348 LOAD_GLOBAL 2 (np) 350 LOAD_METHOD 8 (abs) 352 LOAD_FAST 8 (pivot) 354 CALL_METHOD 1 356 LOAD_CONST 7 (2) 358 COMPARE_OP 2 (==) 360 EXTENDED_ARG 1 362 POP_JUMP_IF_TRUE 388 364 LOAD_GLOBAL 2 (np) 366 LOAD_METHOD 8 (abs) 368 LOAD_FAST 8 (pivot) 370 CALL_METHOD 1 372 LOAD_FAST 2 (PH) 374 LOAD_CONST 7 (2) 376 BINARY_SUBTRACT 378 COMPARE_OP 2 (==) 380 EXTENDED_ARG 1 382 POP_JUMP_IF_TRUE 388 384 LOAD_ASSERTION_ERROR 386 RAISE_VARARGS 1 31 >> 388 LOAD_FAST 8 (pivot) 390 LOAD_CONST 7 (2) 392 INPLACE_FLOOR_DIVIDE 394 STORE_FAST 8 (pivot) 32 >> 396 LOAD_FAST 0 (matrix) 398 LOAD_FAST 4 (col) 400 DUP_TOP_TWO 402 BINARY_SUBSCR 404 LOAD_FAST 8 (pivot) 406 INPLACE_MULTIPLY 408 ROT_THREE 410 STORE_SUBSCR 33 412 LOAD_FAST 0 (matrix) 414 LOAD_FAST 4 (col) 416 DUP_TOP_TWO 418 BINARY_SUBSCR 420 LOAD_FAST 2 (PH) 422 INPLACE_MODULO 424 ROT_THREE 426 STORE_SUBSCR 35 428 LOAD_GLOBAL 2 (np) 430 LOAD_METHOD 4 (argwhere) 432 LOAD_FAST 0 (matrix) 434 LOAD_CONST 0 (None) 436 LOAD_CONST 0 (None) 438 BUILD_SLICE 2 440 LOAD_FAST 4 (col) 442 LOAD_FAST 3 (extra) 444 BINARY_ADD 446 BUILD_TUPLE 2 448 BINARY_SUBSCR 450 CALL_METHOD 1 452 LOAD_METHOD 5 (flatten) 454 CALL_METHOD 0 456 STORE_FAST 9 (others) 37 458 LOAD_FAST 9 (others) 460 GET_ITER >> 462 FOR_ITER 154 (to 618) 464 STORE_FAST 10 (i) 38 466 LOAD_FAST 10 (i) 468 LOAD_FAST 4 (col) 470 COMPARE_OP 2 (==) 472 EXTENDED_ARG 1 474 POP_JUMP_IF_FALSE 480 476 EXTENDED_ARG 1 478 JUMP_ABSOLUTE 462 39 >> 480 LOAD_FAST 6 (used_for_row) 482 LOAD_FAST 10 (i) 484 DUP_TOP_TWO 486 BINARY_SUBSCR 488 LOAD_FAST 6 (used_for_row) 490 LOAD_FAST 4 (col) 492 BINARY_SUBSCR 494 INPLACE_OR 496 ROT_THREE 498 STORE_SUBSCR 40 500 LOAD_FAST 4 (col) 502 LOAD_FAST 1 (graphlen) 504 COMPARE_OP 0 (<) 506 EXTENDED_ARG 2 508 POP_JUMP_IF_FALSE 548 41 510 LOAD_FAST 0 (matrix) 512 LOAD_FAST 10 (i) 514 DUP_TOP_TWO 516 BINARY_SUBSCR 518 LOAD_FAST 0 (matrix) 520 LOAD_FAST 4 (col) 522 BINARY_SUBSCR 524 LOAD_FAST 0 (matrix) 526 LOAD_FAST 10 (i) 528 LOAD_FAST 4 (col) 530 LOAD_FAST 3 (extra) 532 BINARY_ADD 534 BUILD_TUPLE 2 536 BINARY_SUBSCR 538 BINARY_MULTIPLY 540 INPLACE_SUBTRACT 542 ROT_THREE 544 STORE_SUBSCR 546 JUMP_FORWARD 50 (to 598) 43 >> 548 LOAD_FAST 0 (matrix) 550 LOAD_FAST 10 (i) 552 LOAD_FAST 4 (col) 554 LOAD_FAST 3 (extra) 556 BINARY_ADD 558 BUILD_TUPLE 2 560 BINARY_SUBSCR 562 LOAD_CONST 2 (0) 564 COMPARE_OP 3 (!=) 566 EXTENDED_ARG 2 568 POP_JUMP_IF_FALSE 598 44 570 LOAD_FAST 0 (matrix) 572 LOAD_FAST 10 (i) 574 BINARY_SUBSCR 576 LOAD_FAST 0 (matrix) 578 LOAD_FAST 4 (col) 580 BINARY_SUBSCR 582 BINARY_SUBTRACT 584 LOAD_FAST 2 (PH) 586 BINARY_MODULO 588 LOAD_FAST 0 (matrix) 590 LOAD_FAST 10 (i) 592 STORE_SUBSCR 594 EXTENDED_ARG 2 596 JUMP_ABSOLUTE 548 45 >> 598 LOAD_FAST 0 (matrix) 600 LOAD_FAST 10 (i) 602 DUP_TOP_TWO 604 BINARY_SUBSCR 606 LOAD_FAST 2 (PH) 608 INPLACE_MODULO 610 ROT_THREE 612 STORE_SUBSCR 614 EXTENDED_ARG 1 616 JUMP_ABSOLUTE 462 47 >> 618 LOAD_FAST 4 (col) 620 LOAD_CONST 5 (1) 622 INPLACE_ADD 624 STORE_FAST 4 (col) 626 JUMP_ABSOLUTE 48 49 >> 628 LOAD_GLOBAL 2 (np) 630 LOAD_METHOD 9 (array) 632 LOAD_FAST 0 (matrix) 634 CALL_METHOD 1 636 LOAD_FAST 2 (PH) 638 BINARY_MODULO 640 STORE_FAST 0 (matrix) 50 642 LOAD_CONST 8 (<code object <listcomp> at 0x101093b30, file "/private/tmp/a.py", line 50>) 644 LOAD_CONST 4 ('rref.<locals>.<listcomp>') 646 MAKE_FUNCTION 0 648 LOAD_FAST 6 (used_for_row) 650 GET_ITER 652 CALL_FUNCTION 1 654 RETURN_VALUE Disassembly of <code object <listcomp> at 0x101093660, file "/private/tmp/a.py", line 8>: 8 0 BUILD_LIST 0 2 LOAD_FAST 0 (.0) >> 4 FOR_ITER 14 (to 20) 6 STORE_FAST 1 (i) 8 LOAD_GLOBAL 0 (set) 10 LOAD_FAST 1 (i) 12 BUILD_LIST 1 14 CALL_FUNCTION 1 16 LIST_APPEND 2 18 JUMP_ABSOLUTE 4 >> 20 RETURN_VALUE Disassembly of <code object <listcomp> at 0x101093b30, file "/private/tmp/a.py", line 50>: 50 0 BUILD_LIST 0 2 LOAD_FAST 0 (.0) >> 4 FOR_ITER 12 (to 18) 6 STORE_FAST 1 (x) 8 LOAD_GLOBAL 0 (sorted) 10 LOAD_FAST 1 (x) 12 CALL_FUNCTION 1 14 LIST_APPEND 2 16 JUMP_ABSOLUTE 4 >> 18 RETURN_VALUE

LLM Generation

Query

Output

Here is the disassembled code reassembled into a function called `foo`:

import numpy as np

def foo(matrix, PH, graphlen):
    """
    Function to perform row reduction.
    
    Args:
        matrix (numpy.ndarray): Input matrix.
        PH (int): Modulus value.
        graphlen (int): Length of the graph.
    """
    col = 0
    extra = 0
    pivots = []
    used_for_row = np.array([set() for _ in range(matrix.shape[0])])

    while col + extra < matrix.shape[1]:
        # Check if the current column has a non-zero entry
        if matrix[:, col + extra].any():
            # Find the row with a non-zero entry in the current column
            other = np.argwhere(matrix[:, col + extra] != 0).flatten()[-1]

            # Swap rows to move the non-zero entry to the top
            matrix[[other, col], :] = matrix[[col, other], :]
            used_for_row[[other, col]] = used_for_row[[col, other]]

        # Check if the pivot element is 1 or -1
        pivot = matrix[col, col + extra]
        if np.abs(pivot) == 1:
            # Normalize the pivot row
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        elif np.abs(pivot) == 2:
            # Normalize the pivot row
            pivot //= 2
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        else:
            raise AssertionError("Pivot element must be 1 or -1")

        # Eliminate the pivot column
        others = np.argwhere(matrix[:, col + extra] != 0).flatten()
        for i in others:
            if i != col:
                # Subtract the pivot row from the current row
                matrix[i, :] -= matrix[col, :] * matrix[i, col + extra]
                matrix[i, :] %= PH

        # Update the used rows
        for i in others:
            used_for_row[i] |= used_for_row[col]

        # Update the pivot list
        pivots.append(col + extra)

        # Move to the next column
        col += 1

    # Return the reduced matrix and the used rows
    return np.array(matrix) % PH, [sorted(x) for x in used_for_row]

# Example usage:
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
PH = 10
graphlen = 3
reduced_matrix, used_rows = foo(matrix, PH, graphlen)
print(reduced_matrix)
print(used_rows)

Note that I've made some assumptions about the original code, such as the type of the input matrix and the modulus value. You may need to modify the code to fit your specific use case. Additionally, I've used the `numpy` library to perform matrix operations, as it is more efficient and convenient than using built-in Python functions.

Extract Code

I extracted the following code from that output:

import numpy as np

def foo(matrix, PH, graphlen):
    """
    Function to perform row reduction.
    
    Args:
        matrix (numpy.ndarray): Input matrix.
        PH (int): Modulus value.
        graphlen (int): Length of the graph.
    """
    col = 0
    extra = 0
    pivots = []
    used_for_row = np.array([set() for _ in range(matrix.shape[0])])

    while col + extra < matrix.shape[1]:
        # Check if the current column has a non-zero entry
        if matrix[:, col + extra].any():
            # Find the row with a non-zero entry in the current column
            other = np.argwhere(matrix[:, col + extra] != 0).flatten()[-1]

            # Swap rows to move the non-zero entry to the top
            matrix[[other, col], :] = matrix[[col, other], :]
            used_for_row[[other, col]] = used_for_row[[col, other]]

        # Check if the pivot element is 1 or -1
        pivot = matrix[col, col + extra]
        if np.abs(pivot) == 1:
            # Normalize the pivot row
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        elif np.abs(pivot) == 2:
            # Normalize the pivot row
            pivot //= 2
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        else:
            raise AssertionError("Pivot element must be 1 or -1")

        # Eliminate the pivot column
        others = np.argwhere(matrix[:, col + extra] != 0).flatten()
        for i in others:
            if i != col:
                # Subtract the pivot row from the current row
                matrix[i, :] -= matrix[col, :] * matrix[i, col + extra]
                matrix[i, :] %= PH

        # Update the used rows
        for i in others:
            used_for_row[i] |= used_for_row[col]

        # Update the pivot list
        pivots.append(col + extra)

        # Move to the next column
        col += 1

    # Return the reduced matrix and the used rows
    return np.array(matrix) % PH, [sorted(x) for x in used_for_row]

# Example usage:
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
PH = 10
graphlen = 3
reduced_matrix, used_rows = foo(matrix, PH, graphlen)
print(reduced_matrix)
print(used_rows)

Run Code Interpreter

Running the following program:

import numpy as np

def foo(matrix, PH, graphlen):
    """
    Function to perform row reduction.
    
    Args:
        matrix (numpy.ndarray): Input matrix.
        PH (int): Modulus value.
        graphlen (int): Length of the graph.
    """
    col = 0
    extra = 0
    pivots = []
    used_for_row = np.array([set() for _ in range(matrix.shape[0])])

    while col + extra < matrix.shape[1]:
        # Check if the current column has a non-zero entry
        if matrix[:, col + extra].any():
            # Find the row with a non-zero entry in the current column
            other = np.argwhere(matrix[:, col + extra] != 0).flatten()[-1]

            # Swap rows to move the non-zero entry to the top
            matrix[[other, col], :] = matrix[[col, other], :]
            used_for_row[[other, col]] = used_for_row[[col, other]]

        # Check if the pivot element is 1 or -1
        pivot = matrix[col, col + extra]
        if np.abs(pivot) == 1:
            # Normalize the pivot row
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        elif np.abs(pivot) == 2:
            # Normalize the pivot row
            pivot //= 2
            matrix[col, :] *= pivot
            matrix[col, :] %= PH
        else:
            raise AssertionError("Pivot element must be 1 or -1")

        # Eliminate the pivot column
        others = np.argwhere(matrix[:, col + extra] != 0).flatten()
        for i in others:
            if i != col:
                # Subtract the pivot row from the current row
                matrix[i, :] -= matrix[col, :] * matrix[i, col + extra]
                matrix[i, :] %= PH

        # Update the used rows
        for i in others:
            used_for_row[i] |= used_for_row[col]

        # Update the pivot list
        pivots.append(col + extra)

        # Move to the next column
        col += 1

    # Return the reduced matrix and the used rows
    return np.array(matrix) % PH, [sorted(x) for x in used_for_row]

# Example usage:
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
PH = 10
graphlen = 3
reduced_matrix, used_rows = foo(matrix, PH, graphlen)
print(reduced_matrix)
print(used_rows)



arr = np.array([[1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -20], [0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, -20], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, -30], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, -20], [1, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -14], [0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, -30], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, -1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -13], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, -20], [0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -13], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -13], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, -10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -13], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -11], [0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, -20], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, -40], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

answer = foo(arr, 37)
expected = [[0, 38], [0, 38], [0, 6, 28, 35, 38], [0, 6, 18, 21, 28, 35, 38], [0, 6, 28, 35, 38], [0, 6, 21, 28, 35, 38], [0, 5, 10, 23, 38], [0, 5, 10, 23, 38], [0, 5, 10, 23, 38], [0, 5, 9, 10, 23, 38], [0, 5, 9, 10, 13, 17, 23, 30, 38], [0, 5, 9, 10, 11, 23, 27, 38], [0, 5, 9, 10, 11, 23, 27, 38], [0, 5, 9, 10, 11, 23, 25, 27, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 24, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 5, 7, 8, 9, 10, 11, 12, 15, 16, 19, 23, 25, 26, 27, 34, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [0, 5, 9, 10, 13, 17, 23, 30, 38], [0, 5, 9, 10, 13, 17, 23, 30, 38], [0, 5, 9, 10, 13, 17, 23, 30, 32, 38], [0, 5, 9, 10, 13, 17, 23, 30, 32, 33, 38], [0, 22, 29, 38], [0, 22, 29, 38], [0, 6, 28, 35, 38], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 19, 20, 21, 23, 25, 26, 27, 28, 31, 34, 35, 36, 37, 38], [14, 18, 21, 28, 35]]
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')

And got the output:

Traceback (most recent call last):
  File "/usr/src/app/main.py", line 67, in <module>
    reduced_matrix, used_rows = foo(matrix, PH, graphlen)
                                ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 40, in foo
    raise AssertionError("Pivot element must be 1 or -1")
AssertionError: Pivot element must be 1 or -1

Substring Evaluation

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