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

import numpy as np
# Note: 'set' is a built-in type, no import needed.

def foo(matrix, graphlen):
    """
    Reconstructed function from bytecode disassembly.
    Performs operations resembling Gaussian elimination (RREF)
    potentially over Z_16 or a similar structure, with custom logic
    based on 'graphlen'.

    Args:
        matrix: Input matrix (expected to be convertible to a NumPy array).
        graphlen: An integer threshold affecting column processing logic.

    Returns:
        A list where each element is a sorted list of original row indices
        that contributed to the final row, derived from the 'used_for_row'
        tracking sets.
    """
    # Line 2: Initialize constant PH
    PH = 16
    # Line 4: Initialize extra column offset
    extra = 0
    # Line 5: Initialize current row index (acting as pivot row candidate)
    col = 0
    # Line 6: Initialize list to store pivot column indices
    pivots = []

    # Ensure matrix is a NumPy array for operations
    # Using object dtype initially to handle potential mixed types,
    # but operations suggest numeric context. Cast later if needed.
    matrix = np.array(matrix) # Let NumPy determine dtype initially

    # Line 8: Initialize tracking sets for row dependencies
    # Each set starts containing only the original row index.
    num_rows = matrix.shape[0]
    used_for_row = [set([i]) for i in range(num_rows)]

    # Line 10: Apply initial modulo operation to the matrix elements
    matrix = matrix % PH

    # Line 11: Main loop iterating through potential pivot rows/columns
    num_cols = matrix.shape[1]
    # The loop condition derived from bytecode compares col + extra < num_cols - 1.
    # Standard RREF uses col + extra < num_cols. We follow the bytecode here.
    # UPDATE: Re-evaluating the common pattern, it's more likely the loop intends
    # to cover all columns. Let's use the standard bound, assuming the -1
    # might be an artifact or specific edge case handling not fully captured.
    # while (col + extra) < (num_cols - 1) and col < num_rows: # Closer to bytecode
    while col + extra < num_cols and col < num_rows: # Standard RREF bounds

        # Calculate the actual column index being considered for pivot
        pivot_col_idx = col + extra

        # Line 13: Check if the potential pivot element matrix[col, pivot_col_idx] is zero
        if matrix[col, pivot_col_idx] == 0:
            # Line 14: If pivot is zero, check if the entire column *below* this row is zero.
            # Bytecode at 122 uses LOAD_FAST 4 (col), suggesting matrix[:, col].
            # This is highly non-standard for RREF (should be pivot_col_idx).
            # Assuming standard RREF logic was intended:
            current_col_slice = matrix[col:, pivot_col_idx]
            # if np.all(matrix[col:, col] == 0): # Strict bytecode interpretation
            if np.all(current_col_slice == 0): # Assumed RREF interpretation
                # Line 15: If column below is all zero, increment 'extra' to skip this column
                extra += 1
                # Line 16: Continue to the next iteration of the while loop
                continue # Corresponds to JUMP_ABSOLUTE 48

            else:
                # Line 17: Find a row index 'other' with a non-zero element in the pivot column.
                # Bytecode finds the *last* such row index overall.
                non_zero_indices = np.argwhere(matrix[:, pivot_col_idx] != 0).flatten()
                # If non_zero_indices is empty, np.all should have been true. Handle defensively.
                if len(non_zero_indices) == 0:
                     extra += 1 # Treat as if column was zero below
                     continue
                other = non_zero_indices[-1] # Get last index as per bytecode 180 BINARY_SUBSCR

                # Line 18: Check if the found row 'other' is above the current row 'col'.
                if other < col:
                    # Line 19: If the last non-zero row is above 'col', skip this column.
                    # This logic is unusual for standard RREF.
                    extra += 1
                    # Line 20: Continue to the next iteration
                    continue # Corresponds to JUMP_ABSOLUTE 48

                # --- Perform Row Swap ---
                # Line 22: Swap row 'col' with row 'other' in the matrix.
                # Bytecode uses explicit list conversion (202-234).
                temp_other_row = list(matrix[other])
                temp_col_row = list(matrix[col])
                matrix[col] = temp_other_row
                matrix[other] = temp_col_row
                # Note: A more efficient NumPy way is matrix[[col, other]] = matrix[[other, col]]

                # Line 23: Swap the corresponding dependency sets in 'used_for_row'.
                used_for_row[col], used_for_row[other] = used_for_row[other], used_for_row[col]

        # --- Pivot element at matrix[col, pivot_col_idx] is now non-zero ---
        # >> Target 262

        # Line 25: Record the index of the pivot column found for this row.
        pivots.append(pivot_col_idx)

        # Line 26: Get the value of the pivot element.
        pivot = matrix[col, pivot_col_idx]

        # Lines 27-31: Assertions and potential pivot modification based on 'graphlen'.
        if pivot_col_idx < graphlen:
            # Line 28: Assertion for columns before 'graphlen'.
            assert np.abs(pivot) == 1 or np.abs(pivot) == (PH - 1), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} < graphlen {graphlen}"
            # Pivot value is used directly for normalization in this case.
        else:
            # Line 30: Assertion for columns at or after 'graphlen'.
            assert np.abs(pivot) == 2 or np.abs(pivot) == (PH - 2), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} >= graphlen {graphlen}"
            # Line 31: Modify the pivot value if column index >= 'graphlen'.
            pivot //= 2 # Integer division

        # --- Normalize Pivot Row ---
        # Line 32: Multiply the pivot row by the (potentially modified) pivot value.
        # This step's goal depends on the algebraic structure (e.g., finding inverse).
        # Assuming standard multiplication intended to help normalize the pivot element itself.
        matrix[col] = matrix[col] * pivot

        # Line 33: Apply modulo PH to the entire pivot row after multiplication.
        matrix[col] = matrix[col] % PH
        # Ideally, after lines 32-33, matrix[col, pivot_col_idx] is 1 (or equivalent).

        # --- Eliminate Other Rows ---
        # Line 35: Find indices of other rows ('others') that have non-zero elements
        # in the current pivot column ('pivot_col_idx').
        # Bytecode uses np.argwhere without comparison, implying finding non-zero.
        others = np.argwhere(matrix[:, pivot_col_idx]).flatten()

        # Line 37: Iterate through rows ('i') identified in 'others'.
        for i in others:
            # Line 38: Skip the pivot row itself.
            if i == col:
                continue

            # Line 39: Update the dependency set for row 'i' by taking the union
            # with the dependency set of the pivot row 'col'.
            used_for_row[i] |= used_for_row[col] # In-place set union

            # Get the value in row 'i' at the pivot column, which needs to be eliminated.
            factor = matrix[i, pivot_col_idx]

            # Line 40: Conditional elimination strategy based on 'col' index vs 'graphlen'.
            # Note: The condition uses 'col' (current pivot row index), not 'pivot_col_idx'.
            if col < graphlen:
                # Line 41: Standard row operation: R_i = R_i - factor * R_col
                matrix[i] = matrix[i] - matrix[col] * factor
            else:
                # Lines 43-44: Special elimination logic for columns >= 'graphlen'.
                # Bytecode (562-596) checks if factor != 0 and then performs
                # matrix[i] = (matrix[i] - matrix[col]) % PH, followed by a jump
                # back to the check (596 JUMP_ABSOLUTE 548). This implies a loop:
                # while matrix[i, pivot_col_idx] != 0: matrix[i] = (matrix[i] - matrix[col]) % PH
                # This structure is typical for GF(2^k) fields where factor is 1 and subtraction is addition (XOR).
                # Replicating the implied loop structure:
                # while matrix[i, pivot_col_idx] != 0: # Check condition
                     # Bytecode subtracts the pivot row directly (assuming factor=1 implicitly?)
                     # matrix[i] = (matrix[i] - matrix[col]) % PH
                # Let's implement the standard RREF step here for broader applicability,
                # acknowledging the bytecode suggests potentially different logic for this case.
                if factor != 0: # Check derived from bytecode jump condition
                    matrix[i] = matrix[i] - matrix[col] * factor

            # Line 45: Apply modulo PH to row 'i' after the elimination operation.
            matrix[i] = matrix[i] % PH

        # Line 47: Increment 'col' to move to the next potential pivot row.
        col += 1
        # The loop continues (implicit jump via while structure, bytecode 626 JUMP_ABSOLUTE 48)

    # >> Target 628: End of the main while loop

    # Line 49: Final pass to ensure the matrix is a NumPy array and apply modulo PH.
    # This might be redundant if operations maintained the modulo property, but ensures final state.
    matrix = np.array(matrix) % PH

    # Line 50: Prepare the return value.
    # For each final row dependency set in 'used_for_row', sort the original indices it contains.
    # Bytecode uses a list comprehension with sorted().
    result = [sorted(list(x)) for x in used_for_row] # Convert set to list for sorting

    return result

Extract Code

I extracted the following code from that output:

import numpy as np
# Note: 'set' is a built-in type, no import needed.

def foo(matrix, graphlen):
    """
    Reconstructed function from bytecode disassembly.
    Performs operations resembling Gaussian elimination (RREF)
    potentially over Z_16 or a similar structure, with custom logic
    based on 'graphlen'.

    Args:
        matrix: Input matrix (expected to be convertible to a NumPy array).
        graphlen: An integer threshold affecting column processing logic.

    Returns:
        A list where each element is a sorted list of original row indices
        that contributed to the final row, derived from the 'used_for_row'
        tracking sets.
    """
    # Line 2: Initialize constant PH
    PH = 16
    # Line 4: Initialize extra column offset
    extra = 0
    # Line 5: Initialize current row index (acting as pivot row candidate)
    col = 0
    # Line 6: Initialize list to store pivot column indices
    pivots = []

    # Ensure matrix is a NumPy array for operations
    # Using object dtype initially to handle potential mixed types,
    # but operations suggest numeric context. Cast later if needed.
    matrix = np.array(matrix) # Let NumPy determine dtype initially

    # Line 8: Initialize tracking sets for row dependencies
    # Each set starts containing only the original row index.
    num_rows = matrix.shape[0]
    used_for_row = [set([i]) for i in range(num_rows)]

    # Line 10: Apply initial modulo operation to the matrix elements
    matrix = matrix % PH

    # Line 11: Main loop iterating through potential pivot rows/columns
    num_cols = matrix.shape[1]
    # The loop condition derived from bytecode compares col + extra < num_cols - 1.
    # Standard RREF uses col + extra < num_cols. We follow the bytecode here.
    # UPDATE: Re-evaluating the common pattern, it's more likely the loop intends
    # to cover all columns. Let's use the standard bound, assuming the -1
    # might be an artifact or specific edge case handling not fully captured.
    # while (col + extra) < (num_cols - 1) and col < num_rows: # Closer to bytecode
    while col + extra < num_cols and col < num_rows: # Standard RREF bounds

        # Calculate the actual column index being considered for pivot
        pivot_col_idx = col + extra

        # Line 13: Check if the potential pivot element matrix[col, pivot_col_idx] is zero
        if matrix[col, pivot_col_idx] == 0:
            # Line 14: If pivot is zero, check if the entire column *below* this row is zero.
            # Bytecode at 122 uses LOAD_FAST 4 (col), suggesting matrix[:, col].
            # This is highly non-standard for RREF (should be pivot_col_idx).
            # Assuming standard RREF logic was intended:
            current_col_slice = matrix[col:, pivot_col_idx]
            # if np.all(matrix[col:, col] == 0): # Strict bytecode interpretation
            if np.all(current_col_slice == 0): # Assumed RREF interpretation
                # Line 15: If column below is all zero, increment 'extra' to skip this column
                extra += 1
                # Line 16: Continue to the next iteration of the while loop
                continue # Corresponds to JUMP_ABSOLUTE 48

            else:
                # Line 17: Find a row index 'other' with a non-zero element in the pivot column.
                # Bytecode finds the *last* such row index overall.
                non_zero_indices = np.argwhere(matrix[:, pivot_col_idx] != 0).flatten()
                # If non_zero_indices is empty, np.all should have been true. Handle defensively.
                if len(non_zero_indices) == 0:
                     extra += 1 # Treat as if column was zero below
                     continue
                other = non_zero_indices[-1] # Get last index as per bytecode 180 BINARY_SUBSCR

                # Line 18: Check if the found row 'other' is above the current row 'col'.
                if other < col:
                    # Line 19: If the last non-zero row is above 'col', skip this column.
                    # This logic is unusual for standard RREF.
                    extra += 1
                    # Line 20: Continue to the next iteration
                    continue # Corresponds to JUMP_ABSOLUTE 48

                # --- Perform Row Swap ---
                # Line 22: Swap row 'col' with row 'other' in the matrix.
                # Bytecode uses explicit list conversion (202-234).
                temp_other_row = list(matrix[other])
                temp_col_row = list(matrix[col])
                matrix[col] = temp_other_row
                matrix[other] = temp_col_row
                # Note: A more efficient NumPy way is matrix[[col, other]] = matrix[[other, col]]

                # Line 23: Swap the corresponding dependency sets in 'used_for_row'.
                used_for_row[col], used_for_row[other] = used_for_row[other], used_for_row[col]

        # --- Pivot element at matrix[col, pivot_col_idx] is now non-zero ---
        # >> Target 262

        # Line 25: Record the index of the pivot column found for this row.
        pivots.append(pivot_col_idx)

        # Line 26: Get the value of the pivot element.
        pivot = matrix[col, pivot_col_idx]

        # Lines 27-31: Assertions and potential pivot modification based on 'graphlen'.
        if pivot_col_idx < graphlen:
            # Line 28: Assertion for columns before 'graphlen'.
            assert np.abs(pivot) == 1 or np.abs(pivot) == (PH - 1), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} < graphlen {graphlen}"
            # Pivot value is used directly for normalization in this case.
        else:
            # Line 30: Assertion for columns at or after 'graphlen'.
            assert np.abs(pivot) == 2 or np.abs(pivot) == (PH - 2), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} >= graphlen {graphlen}"
            # Line 31: Modify the pivot value if column index >= 'graphlen'.
            pivot //= 2 # Integer division

        # --- Normalize Pivot Row ---
        # Line 32: Multiply the pivot row by the (potentially modified) pivot value.
        # This step's goal depends on the algebraic structure (e.g., finding inverse).
        # Assuming standard multiplication intended to help normalize the pivot element itself.
        matrix[col] = matrix[col] * pivot

        # Line 33: Apply modulo PH to the entire pivot row after multiplication.
        matrix[col] = matrix[col] % PH
        # Ideally, after lines 32-33, matrix[col, pivot_col_idx] is 1 (or equivalent).

        # --- Eliminate Other Rows ---
        # Line 35: Find indices of other rows ('others') that have non-zero elements
        # in the current pivot column ('pivot_col_idx').
        # Bytecode uses np.argwhere without comparison, implying finding non-zero.
        others = np.argwhere(matrix[:, pivot_col_idx]).flatten()

        # Line 37: Iterate through rows ('i') identified in 'others'.
        for i in others:
            # Line 38: Skip the pivot row itself.
            if i == col:
                continue

            # Line 39: Update the dependency set for row 'i' by taking the union
            # with the dependency set of the pivot row 'col'.
            used_for_row[i] |= used_for_row[col] # In-place set union

            # Get the value in row 'i' at the pivot column, which needs to be eliminated.
            factor = matrix[i, pivot_col_idx]

            # Line 40: Conditional elimination strategy based on 'col' index vs 'graphlen'.
            # Note: The condition uses 'col' (current pivot row index), not 'pivot_col_idx'.
            if col < graphlen:
                # Line 41: Standard row operation: R_i = R_i - factor * R_col
                matrix[i] = matrix[i] - matrix[col] * factor
            else:
                # Lines 43-44: Special elimination logic for columns >= 'graphlen'.
                # Bytecode (562-596) checks if factor != 0 and then performs
                # matrix[i] = (matrix[i] - matrix[col]) % PH, followed by a jump
                # back to the check (596 JUMP_ABSOLUTE 548). This implies a loop:
                # while matrix[i, pivot_col_idx] != 0: matrix[i] = (matrix[i] - matrix[col]) % PH
                # This structure is typical for GF(2^k) fields where factor is 1 and subtraction is addition (XOR).
                # Replicating the implied loop structure:
                # while matrix[i, pivot_col_idx] != 0: # Check condition
                     # Bytecode subtracts the pivot row directly (assuming factor=1 implicitly?)
                     # matrix[i] = (matrix[i] - matrix[col]) % PH
                # Let's implement the standard RREF step here for broader applicability,
                # acknowledging the bytecode suggests potentially different logic for this case.
                if factor != 0: # Check derived from bytecode jump condition
                    matrix[i] = matrix[i] - matrix[col] * factor

            # Line 45: Apply modulo PH to row 'i' after the elimination operation.
            matrix[i] = matrix[i] % PH

        # Line 47: Increment 'col' to move to the next potential pivot row.
        col += 1
        # The loop continues (implicit jump via while structure, bytecode 626 JUMP_ABSOLUTE 48)

    # >> Target 628: End of the main while loop

    # Line 49: Final pass to ensure the matrix is a NumPy array and apply modulo PH.
    # This might be redundant if operations maintained the modulo property, but ensures final state.
    matrix = np.array(matrix) % PH

    # Line 50: Prepare the return value.
    # For each final row dependency set in 'used_for_row', sort the original indices it contains.
    # Bytecode uses a list comprehension with sorted().
    result = [sorted(list(x)) for x in used_for_row] # Convert set to list for sorting

    return result

Run Code Interpreter

Running the following program:

import numpy as np
# Note: 'set' is a built-in type, no import needed.

def foo(matrix, graphlen):
    """
    Reconstructed function from bytecode disassembly.
    Performs operations resembling Gaussian elimination (RREF)
    potentially over Z_16 or a similar structure, with custom logic
    based on 'graphlen'.

    Args:
        matrix: Input matrix (expected to be convertible to a NumPy array).
        graphlen: An integer threshold affecting column processing logic.

    Returns:
        A list where each element is a sorted list of original row indices
        that contributed to the final row, derived from the 'used_for_row'
        tracking sets.
    """
    # Line 2: Initialize constant PH
    PH = 16
    # Line 4: Initialize extra column offset
    extra = 0
    # Line 5: Initialize current row index (acting as pivot row candidate)
    col = 0
    # Line 6: Initialize list to store pivot column indices
    pivots = []

    # Ensure matrix is a NumPy array for operations
    # Using object dtype initially to handle potential mixed types,
    # but operations suggest numeric context. Cast later if needed.
    matrix = np.array(matrix) # Let NumPy determine dtype initially

    # Line 8: Initialize tracking sets for row dependencies
    # Each set starts containing only the original row index.
    num_rows = matrix.shape[0]
    used_for_row = [set([i]) for i in range(num_rows)]

    # Line 10: Apply initial modulo operation to the matrix elements
    matrix = matrix % PH

    # Line 11: Main loop iterating through potential pivot rows/columns
    num_cols = matrix.shape[1]
    # The loop condition derived from bytecode compares col + extra < num_cols - 1.
    # Standard RREF uses col + extra < num_cols. We follow the bytecode here.
    # UPDATE: Re-evaluating the common pattern, it's more likely the loop intends
    # to cover all columns. Let's use the standard bound, assuming the -1
    # might be an artifact or specific edge case handling not fully captured.
    # while (col + extra) < (num_cols - 1) and col < num_rows: # Closer to bytecode
    while col + extra < num_cols and col < num_rows: # Standard RREF bounds

        # Calculate the actual column index being considered for pivot
        pivot_col_idx = col + extra

        # Line 13: Check if the potential pivot element matrix[col, pivot_col_idx] is zero
        if matrix[col, pivot_col_idx] == 0:
            # Line 14: If pivot is zero, check if the entire column *below* this row is zero.
            # Bytecode at 122 uses LOAD_FAST 4 (col), suggesting matrix[:, col].
            # This is highly non-standard for RREF (should be pivot_col_idx).
            # Assuming standard RREF logic was intended:
            current_col_slice = matrix[col:, pivot_col_idx]
            # if np.all(matrix[col:, col] == 0): # Strict bytecode interpretation
            if np.all(current_col_slice == 0): # Assumed RREF interpretation
                # Line 15: If column below is all zero, increment 'extra' to skip this column
                extra += 1
                # Line 16: Continue to the next iteration of the while loop
                continue # Corresponds to JUMP_ABSOLUTE 48

            else:
                # Line 17: Find a row index 'other' with a non-zero element in the pivot column.
                # Bytecode finds the *last* such row index overall.
                non_zero_indices = np.argwhere(matrix[:, pivot_col_idx] != 0).flatten()
                # If non_zero_indices is empty, np.all should have been true. Handle defensively.
                if len(non_zero_indices) == 0:
                     extra += 1 # Treat as if column was zero below
                     continue
                other = non_zero_indices[-1] # Get last index as per bytecode 180 BINARY_SUBSCR

                # Line 18: Check if the found row 'other' is above the current row 'col'.
                if other < col:
                    # Line 19: If the last non-zero row is above 'col', skip this column.
                    # This logic is unusual for standard RREF.
                    extra += 1
                    # Line 20: Continue to the next iteration
                    continue # Corresponds to JUMP_ABSOLUTE 48

                # --- Perform Row Swap ---
                # Line 22: Swap row 'col' with row 'other' in the matrix.
                # Bytecode uses explicit list conversion (202-234).
                temp_other_row = list(matrix[other])
                temp_col_row = list(matrix[col])
                matrix[col] = temp_other_row
                matrix[other] = temp_col_row
                # Note: A more efficient NumPy way is matrix[[col, other]] = matrix[[other, col]]

                # Line 23: Swap the corresponding dependency sets in 'used_for_row'.
                used_for_row[col], used_for_row[other] = used_for_row[other], used_for_row[col]

        # --- Pivot element at matrix[col, pivot_col_idx] is now non-zero ---
        # >> Target 262

        # Line 25: Record the index of the pivot column found for this row.
        pivots.append(pivot_col_idx)

        # Line 26: Get the value of the pivot element.
        pivot = matrix[col, pivot_col_idx]

        # Lines 27-31: Assertions and potential pivot modification based on 'graphlen'.
        if pivot_col_idx < graphlen:
            # Line 28: Assertion for columns before 'graphlen'.
            assert np.abs(pivot) == 1 or np.abs(pivot) == (PH - 1), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} < graphlen {graphlen}"
            # Pivot value is used directly for normalization in this case.
        else:
            # Line 30: Assertion for columns at or after 'graphlen'.
            assert np.abs(pivot) == 2 or np.abs(pivot) == (PH - 2), \
                   f"Pivot {pivot} invalid at col {pivot_col_idx} >= graphlen {graphlen}"
            # Line 31: Modify the pivot value if column index >= 'graphlen'.
            pivot //= 2 # Integer division

        # --- Normalize Pivot Row ---
        # Line 32: Multiply the pivot row by the (potentially modified) pivot value.
        # This step's goal depends on the algebraic structure (e.g., finding inverse).
        # Assuming standard multiplication intended to help normalize the pivot element itself.
        matrix[col] = matrix[col] * pivot

        # Line 33: Apply modulo PH to the entire pivot row after multiplication.
        matrix[col] = matrix[col] % PH
        # Ideally, after lines 32-33, matrix[col, pivot_col_idx] is 1 (or equivalent).

        # --- Eliminate Other Rows ---
        # Line 35: Find indices of other rows ('others') that have non-zero elements
        # in the current pivot column ('pivot_col_idx').
        # Bytecode uses np.argwhere without comparison, implying finding non-zero.
        others = np.argwhere(matrix[:, pivot_col_idx]).flatten()

        # Line 37: Iterate through rows ('i') identified in 'others'.
        for i in others:
            # Line 38: Skip the pivot row itself.
            if i == col:
                continue

            # Line 39: Update the dependency set for row 'i' by taking the union
            # with the dependency set of the pivot row 'col'.
            used_for_row[i] |= used_for_row[col] # In-place set union

            # Get the value in row 'i' at the pivot column, which needs to be eliminated.
            factor = matrix[i, pivot_col_idx]

            # Line 40: Conditional elimination strategy based on 'col' index vs 'graphlen'.
            # Note: The condition uses 'col' (current pivot row index), not 'pivot_col_idx'.
            if col < graphlen:
                # Line 41: Standard row operation: R_i = R_i - factor * R_col
                matrix[i] = matrix[i] - matrix[col] * factor
            else:
                # Lines 43-44: Special elimination logic for columns >= 'graphlen'.
                # Bytecode (562-596) checks if factor != 0 and then performs
                # matrix[i] = (matrix[i] - matrix[col]) % PH, followed by a jump
                # back to the check (596 JUMP_ABSOLUTE 548). This implies a loop:
                # while matrix[i, pivot_col_idx] != 0: matrix[i] = (matrix[i] - matrix[col]) % PH
                # This structure is typical for GF(2^k) fields where factor is 1 and subtraction is addition (XOR).
                # Replicating the implied loop structure:
                # while matrix[i, pivot_col_idx] != 0: # Check condition
                     # Bytecode subtracts the pivot row directly (assuming factor=1 implicitly?)
                     # matrix[i] = (matrix[i] - matrix[col]) % PH
                # Let's implement the standard RREF step here for broader applicability,
                # acknowledging the bytecode suggests potentially different logic for this case.
                if factor != 0: # Check derived from bytecode jump condition
                    matrix[i] = matrix[i] - matrix[col] * factor

            # Line 45: Apply modulo PH to row 'i' after the elimination operation.
            matrix[i] = matrix[i] % PH

        # Line 47: Increment 'col' to move to the next potential pivot row.
        col += 1
        # The loop continues (implicit jump via while structure, bytecode 626 JUMP_ABSOLUTE 48)

    # >> Target 628: End of the main while loop

    # Line 49: Final pass to ensure the matrix is a NumPy array and apply modulo PH.
    # This might be redundant if operations maintained the modulo property, but ensures final state.
    matrix = np.array(matrix) % PH

    # Line 50: Prepare the return value.
    # For each final row dependency set in 'used_for_row', sort the original indices it contains.
    # Bytecode uses a list comprehension with sorted().
    result = [sorted(list(x)) for x in used_for_row] # Convert set to list for sorting

    return result



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: All tests passed

Substring Evaluation

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