Reliable Shop Tools: Unit B Worked Solution

Instructor worked edition · 90 minutes of dedicated work · 2026-09-09

This is one of two practical units for Chapter 2. Unit A constructs and connects the mechanism; Unit B investigates a controlled failure, repairs it and transfers the invariant. Each is a complete ninety-minute session, with its own setup and required conceptual introductions. Basic Python variables, conditions, loops, functions, lists and dictionaries are the starting knowledge. Libraries and specialized concepts used here are introduced below before the main task.

By the end you should be able to:

1. Explain the chapter's mechanism using a prediction and an observed intermediate result.
2. Repair the failure: An integer quantity can be schema-valid and still over-order. Compare schema refusal, business-rule refusal and successful calculation. Connect build_tools to the same Dispatcher used by the Chapter 3 loop.
3. Solve use one reservation rule in reporting and drafting using changed inputs and an independent expectation.
4. Retain your implementation, failed/corrected observations, causal explanation and limits.

Installation is preparation time. These are planning estimates, not measured completion times. Use the reference primers when a term is unfamiliar; in Unit B retrieve an explanation before re-reading it. Run All checks that the artifact executes. Unfinished student functions deliberately produce NEEDS_WORK. Keep your first attempt before opening answers.

This notebook belongs to the nineteen-chapter edition. Its supplied teaching runtime is embedded, so it can run without the textbook or another notebook. Where code uses REFERENCE_LESSON, that is the frozen runtime exercise identifier; the reader-facing chapter and saved unit identifiers use the current edition. Building against a supplied runtime is not proof that you have constructed all of its dependencies.

Run the self-contained setup

Use a Python 3.14 Jupyter kernel and Pydantic 2. If needed, run %pip install "pydantic==2.13.4" once in a separate cell and restart the kernel. Package installation needs internet; the lesson itself needs no repository download, API key or prior notebook. The complete source runtime uses Python 3.14, so this edition does not claim compatibility with a hosted notebook service's default interpreter.

The collapsed cell contains 102 frozen teaching files. Base85 represents compressed bytes as text; zlib decompresses them; SHA-256 checks that the decoded files match this edition. These are supplied packaging operations, not learner algorithms. tempfile creates an isolated working copy; Path handles file locations; sys.path tells Python where the supplied modules live. The code is available for inspection below and performs no package installation itself. The subsequent lesson teaches the libraries used by the mechanisms you will implement.

Run setup on every fresh kernel. It writes scratch runtime files separately from your retained practical-work/ch02-b folder. Rerunning setup restores the frozen support files and keeps your saved work. Restarting a kernel clears variables, not saved submission files. Source basis: Sovereign Agent 444c5f6. Some tasks use reviewed local subprocesses; they are not an OS sandbox.

Supplied offline setup and teaching files

The complete supplied setup cell is in the downloadable notebook. Run it there before attempting the cells below. This web edition omits only that compressed setup payload.

Commit to a prediction before the examples

prediction_notes = {
    "prediction": "Write the expected behavior before running the worked example.",
    "reason": "Name the input and rule behind that prediction.",
    "falsifier": "Name an observation that would prove the explanation wrong.",
    "revision": "After execution, explain what changed in your understanding.",
}

Reading the Python vocabulary used in this notebook

You need basic assignments, if, loops, functions, lists and dictionaries. The less familiar features used by the supplied code are introduced here. A library is reusable code that Python can import. The standard library ships with Python; Pydantic is an additional package. An import makes a name available, but does not mean that you have completed the exercise.

JSON is text for exchanging structured values. A Python dictionary is an in-memory object; the JSON representation is a string. Use json.dumps to encode and json.loads to decode. Decoding proves that text has valid JSON syntax, not that its fields match our business contract. Predict which of the following two decoded objects could describe a stock count.

import json

intro_data = {"sku": "MANGO", "count": 3}
intro_text = json.dumps(intro_data, sort_keys=True)
print(type(intro_data).__name__, type(intro_text).__name__, intro_text)
print(json.loads(intro_text))
print("Also valid JSON:", json.loads('["not", "a", "stock", "record"]'))
assert json.loads(intro_text) == intro_data

The first result is a dictionary; the second is a list. Before indexing a decoded object, check the shape that your function promises to accept. An exception interrupts the normal path. raise ValueError(...) refuses an invalid value; try/except lets a caller inspect that expected refusal. Catch the expected class, rather than turning every programming error into apparent success. finally runs cleanup even when an earlier operation raises.

An annotation, such as count: int, documents the expected type. It does not by itself enforce the type at runtime. A class defines a kind of object; an instance holds one object's data. @dataclass asks Python to generate routine construction and comparison methods from annotated fields. frozen=True prevents ordinary reassignment of the instance's fields; it does not make every object nested inside those fields immutable. A method is a function attached to a class; self refers to the instance receiving the call.

from dataclasses import dataclass


@dataclass(frozen=True)
class IntroObservation:
    operation: str
    count: int


intro_observation = IntroObservation("count-mango", 3)
print(intro_observation.operation, intro_observation.count)
assert intro_observation == IntroObservation("count-mango", 3)

A callback is a function passed to another function. This is how the classroom harness invokes your implementation. The argument candidate below is a function object; parentheses perform the call. Predict the two answers before execution, then trace the result to the callback.

def intro_apply(candidate, value):
    return {"input": value, "observed": candidate(value)}


def intro_double(value):
    return value * 2


print(intro_apply(intro_double, 3))
print(intro_apply(lambda value: value + 2, 3))
assert intro_apply(intro_double, 3)["observed"] == 6

lambda value: value + 2 is a small anonymous function. A closure is a function that retains access to values from its surrounding scope. It can bind a tool to a shop snapshot. A shallow copy duplicates only the outer container; copy.deepcopy also copies nested containers used in these fixtures. A set stores distinct values; required <= allowed asks whether every required item is allowed. frozenset is the corresponding immutable set. A tuple groups ordered values; (value,) is a one-item tuple, including the comma.

Paths and cleanup. Path represents a filesystem location. path / "file.json" constructs a child path; read_text and write_text read and write text. A context manager, used with with, manages entry and exit. A temporary-directory context removes its contents on exit. Save your submission outside temporary runtime directories. Reopening a file is different from reusing a Python variable: the former tests retained bytes, while the latter only tests this kernel.

from pathlib import Path
from tempfile import TemporaryDirectory

with TemporaryDirectory() as intro_folder:
    intro_path = Path(intro_folder) / "observation.json"
    intro_path.write_text(json.dumps(intro_data), encoding="utf-8")
    intro_reopened = json.loads(intro_path.read_text(encoding="utf-8"))
    assert intro_reopened == intro_data
    print("Read from a file:", intro_reopened)

Retrieval check: explain JSON versus a dictionary, annotation versus validation, class versus instance, and defining a callback versus invoking it. Change the callback above so an incorrect implementation visibly changes the observed output. This distinction will matter when grading your connected work. Reference: Python's JSON, dataclasses, and pathlib documentation.

from pydantic import BaseModel, ConfigDict, Field, ValidationError

Pydantic from first principles · 20-minute guided introduction

No previous Pydantic experience is assumed. We start with familiar Python dictionaries, then build a small contract, inspect failures, and connect that contract to a tool call. Pydantic is a Python library for checking data against declared types and constraints. Here, model means a class describing data; it is not a neural network or language model.

By the end, you should be able to define a model, validate a dictionary, explain a validation error, reject unwanted conversions, and distinguish a schema from a business rule. This complete introduction supports either independent ninety-minute unit. Its small repair checkpoint is preparation for the full tool-factory and transfer exercises.

1. Why annotations alone are not a boundary · minutes 0–2

A caller sends a quantity as a string. A type annotation documents what we expect, but Python does not automatically check that expectation when the function runs. Predict: will this call raise an error, return twelve, or repeat the characters?

def annotated_double(quantity: int) -> int:
    return quantity * 2


incoming_quantity = "6"
observed = annotated_double(incoming_quantity)
print(repr(observed), type(observed).__name__)
assert observed == "66"

The result is '66', a string. The annotation did not become a runtime guard. A type checker can flag this example before execution, but external data still needs a runtime boundary. We will check it before passing it to calculations that assume validated input.

2. A model is a recipe; an instance holds data · minutes 2–5

Read class IntroLine(BaseModel) as “define our data class using Pydantic's validation machinery.” The indented annotations declare fields, the named pieces of data. sku and quantity have no defaults and must be supplied. note has a default, so callers can omit it. str and int are ordinary Python types. No decorators or inheritance theory are needed to use this pattern: declare the fields inside a class that inherits from BaseModel.

The class is the reusable recipe; line is one validated instance. The constructor accepts named arguments. model_validate accepts a dictionary, which is convenient when a tool caller has already produced one. Dot notation reads the instance's fields. Predict the missing note.

class IntroLine(BaseModel):
    sku: str
    quantity: int
    note: str = ""


line = IntroLine(sku="SKU-VANILLA", quantity=6)
incoming = {"sku": "SKU-VANILLA", "quantity": 6}
parsed_line = IntroLine.model_validate(incoming)
print(line.sku, line.quantity, repr(line.note))
print("Same values:", line == parsed_line)
assert line.note == "" and line == parsed_line

3. Read an error as a repair instruction · minutes 5–7

Now omit sku and supply a quantity that cannot be parsed as an integer. Pydantic raises ValidationError instead of returning a usable instance. Catch this expected exception so the notebook can show the failure and continue. Its errors() method returns structured details: loc identifies the field, type classifies the failure, and msg explains it. We hide the raw input in this display; we need the location and rule to understand the failure.

Predict: how many fields need repair? A missing required field is different from a field with an invalid value. Read every reported error, not just the first line.

bad_line = {"quantity": "six"}
try:
    IntroLine.model_validate(bad_line)
except ValidationError as error:
    details = error.errors(include_url=False, include_input=False)
    for detail in details:
        print(detail["loc"], detail["type"], detail["msg"])
    assert {item["loc"] for item in details} == {("sku",), ("quantity",)}
else:
    raise AssertionError("The invalid line was accepted")

Both fields need attention. An exception is useful feedback here, not a notebook setup failure. In the tool dispatcher later, this same exception becomes a refused observation. Validation reports the problem; it does not decide whether to ask the caller to correct it.

4. Accepted input can be converted input · minutes 7–10

By default, Pydantic may convert compatible input into the declared type. That is helpful for a form but can hide what a tool caller actually sent. For an integer field, compare 6, "6", True, 6.0, and "six". Write the two acceptance columns before running. The strict=True argument asks this validation call to reject these integer conversions. This table concerns Python inputs to an integer field; strict handling of JSON dates and other types has additional rules. We do not generalize this table to every type.

class IntroQuantity(BaseModel):
    quantity: int


def inspect_quantity(value, strict):
    try:
        result = IntroQuantity.model_validate({"quantity": value}, strict=strict)
    except ValidationError:
        return "REFUSED"
    return f"{result.quantity!r} ({type(result.quantity).__name__})"


quantity_inputs = [6, "6", True, 6.0, "six"]
for value in quantity_inputs:
    print(
        repr(value),
        "| default:",
        inspect_quantity(value, False),
        "| strict:",
        inspect_quantity(value, True),
    )
assert [inspect_quantity(value, True) != "REFUSED" for value in quantity_inputs] == [
    True,
    False,
    False,
    False,
    False,
]
boolean_quantity = True
print("Python considers bool an int subclass:", isinstance(boolean_quantity, int))
print("Its exact type is int:", type(boolean_quantity) is int)

Default validation converts the first four values to integers. The strict column accepts only 6. Python's boolean/integer relationship explains why a casual isinstance(value, int) check would miss one of these cases. Pydantic's strict integer validation rejects a boolean. Choose the conversion policy deliberately at the boundary.

5. Types, constraints, and model policy do different jobs · minutes 10–13

Our stock-reading contract has three layers:

ge means greater than or equal; gt means strictly greater. le means less than or equal. For strings, min_length and max_length constrain character count. Here Field(...) adds constraints; it does not supply a field value. model_config configures validation; it is not an input field. Pydantic otherwise ignores extra keys by default, so we explicitly forbid them when every accepted argument should belong to the declared contract.

Predict: zero tubs is valid stock. Should negative stock, a string count, or an extra approved key pass this contract? The stock example is different from an order, which will require a positive quantity in Exercise 1.

class IntroStockReading(BaseModel):
    model_config = ConfigDict(strict=True, extra="forbid")
    sku: str = Field(min_length=1, max_length=100)
    on_hand: int = Field(ge=0, le=1000)


stock_examples = [
    ("empty shelf", {"sku": "SKU-VANILLA", "on_hand": 0}, True),
    ("negative count", {"sku": "SKU-VANILLA", "on_hand": -1}, False),
    ("string count", {"sku": "SKU-VANILLA", "on_hand": "2"}, False),
    ("extra key", {"sku": "SKU-VANILLA", "on_hand": 2, "approved": True}, False),
]
for label, payload, expected in stock_examples:
    try:
        validated = IntroStockReading.model_validate(payload)
        accepted = True
        print(label, "ACCEPTED", validated.model_dump())
    except ValidationError as error:
        accepted = False
        print(label, "REFUSED", [item["type"] for item in error.errors()])
    assert accepted == expected

6. Dictionary, instance, JSON, schema · minutes 13–16

These objects serve different purposes. A dictionary contains Python data. A model instance provides validated fields. JSON text is a portable string representation. A JSON Schema describes the expected shape so another program can discover the contract.

For these string/integer fields, the round trip preserves the values. Other field types can need different serialization choices. The schema below describes possible stock readings; it does not contain the current reading, validate a caller by itself, or execute a handler. Predict: which output contains the actual count, and which contains its minimum?

reading = IntroStockReading(sku="SKU-VANILLA", on_hand=2)
as_dict = reading.model_dump()
as_json = reading.model_dump_json()
schema_description = IntroStockReading.model_json_schema()
print("Dictionary:", as_dict)
print("JSON string:", repr(as_json))
print("Count description:", schema_description["properties"]["on_hand"])
print("Required fields:", schema_description["required"])
assert isinstance(as_dict, dict) and isinstance(as_json, str)
assert IntroStockReading.model_validate_json(as_json) == reading
assert schema_description["properties"]["on_hand"]["minimum"] == 0
assert schema_description["additionalProperties"] is False

7. Validation is a contract check, not a fact check · minutes 16–17

An invented count of 999 satisfies our type and range rules. Pydantic has no connection to Lucy's shelf and cannot know whether that count is true. We must compare it with an authoritative source separately. Similarly, validating an approved field in some other schema would not establish that a real person authorized a purchase.

The model also does not promise that ordinary assignments to an existing instance are revalidated: assignment validation is a separate configuration option. In this lesson we validate new input at the boundary and then use its fields; we do not use unvalidated updates.

plausible_but_unverified = IntroStockReading(sku="SKU-VANILLA", on_hand=999)
authoritative_count = 2
print("Passes the declared contract:", plausible_but_unverified.on_hand)
print("Matches the shop record:", plausible_but_unverified.on_hand == authoritative_count)
assert plausible_but_unverified.on_hand != authoritative_count

8. Your turn: repair the data without weakening the contract · minutes 17–20

The authoritative record says SKU SKU-VANILLA has 2 tubs. Repair repaired_stock in the next cell so it represents that record and passes IntroStockReading. Keep the model unchanged. Before running, name all three problems with the supplied dictionary. Explain why changing a type annotation to Any would hide a problem rather than repair this data.

Hint 1: inspect the required names and each value's Python type. Hint 2: an empty string is still a string, but the length constraint matters. Hint 3: the data contract has exactly two fields. A proposed approval is not stock data.

repaired_stock = {"sku": "SKU-VANILLA", "on_hand": 2}

try:
    repaired_reading = IntroStockReading.model_validate(repaired_stock)
except ValidationError as error:
    pydantic_checkpoint = False
    for detail in error.errors(include_url=False, include_input=False):
        print(detail["loc"], detail["msg"])
else:
    pydantic_checkpoint = repaired_reading.model_dump() == {
        "sku": "SKU-VANILLA",
        "on_hand": 2,
    }
print("PASS" if pydantic_checkpoint else "NEEDS_WORK", "Pydantic data-repair checkpoint")

Retrieve the mechanism before continuing

Close the examples and explain: (1) what makes a field required, (2) why "6" can pass one integer model and fail another, (3) what loc tells you, (4) how a schema differs from an instance, and (5) why a valid count can still be false. Use your observed outputs as evidence.

Vocabulary: a field is one named data item; a constraint limits its allowed values; coercion converts input; validation checks the declared contract; serialization converts an instance to a representation for storage or transport. None of these grants permission.

Connection to our shop tools: NoArguments accepts an empty argument dictionary; ProductArguments checks a SKU; DraftArguments also checks a positive quantity. Each follows the same BaseModel pattern you have just used. A tool's parameters points to one of these classes. The dispatcher calls tool.parameters.model_validate(call.arguments) before handing the resulting instance to the handler. Later, trace that exact call in the code. The model owns structural checks; shop records and the handler own the business calculation; the allowlist and authority callback decide whether execution may proceed.

Official Pydantic 2 references for further reading: models, fields, strict mode, validation errors, serialization, and JSON Schema.

From a Python function to a shop tool

A tool is a program operation exposed through a named interface. In this chapter the stock tool reads a copied shop snapshot, the supplier tool reads a price, and the draft tool calculates an order proposal. The interface has a name, an argument contract and a handler. The handler is the Python function that actually performs the operation. Advertising a schema describes possible arguments; invoking a handler causes behavior.

Pydantic is introduced in full below. As you work through it, keep three independent questions in view: is the argument well formed, does it agree with the current business rule, and is this caller permitted to execute the operation? A positive integer can pass the first question and fail the second. A valid request can pass both and still be outside the caller's allowlist.

Calculate before designing an interface

Work with integer pence so multiplication does not introduce a floating-point rounding question. The SKU is a stable product identifier; the display name may change without changing identity. An empty catalog means no products. Two rows with the same SKU are ambiguous and must not silently overwrite each other during construction.

intro_shop = [
    {"sku": "MANGO", "on_hand": 1, "target": 5, "price_pence": 325},
    {"sku": "COCOA", "on_hand": 9, "target": 6, "price_pence": 300},
]
for intro_product in intro_shop:
    intro_needed = max(0, intro_product["target"] - intro_product["on_hand"])
    intro_total = intro_needed * intro_product["price_pence"]
    print(intro_product["sku"], intro_needed, intro_total)
assert 4 * 325 == 1300

Mango needs four tubs, costing 1300 pence. Cocoa needs none. A draft requesting three mango tubs is type-correct but inconsistent with our exact-need contract. The handler must obtain the authoritative price itself; accepting a caller's invented total would move the calculation's authority to untrusted input.

Observe why a snapshot is copied

A closure can retain access to a shop dictionary. Without a deep copy, another part of the program can mutate that dictionary after the tools are built and silently change what an existing tool sees. A copied snapshot makes this particular experiment repeatable. It does not promise that a real shop never changes; refreshing stale stock is a separate contract.

import copy

intro_original = {"MANGO": {"on_hand": 1, "target": 5}}
intro_snapshot = copy.deepcopy(intro_original)


def intro_stock_handler():
    return copy.deepcopy(intro_snapshot)


intro_original["MANGO"]["on_hand"] = 20
intro_observed_stock = intro_stock_handler()
assert intro_observed_stock["MANGO"]["on_hand"] == 1
intro_observed_stock["MANGO"]["on_hand"] = 99
assert intro_stock_handler()["MANGO"]["on_hand"] == 1
print("Neither external input mutation nor output mutation changed the retained snapshot.")

The two copies protect different directions: the first prevents mutation through the original input; the second prevents mutation through a returned result. Explain both before writing the factory. A factory is simply a function that constructs and returns configured objects.

Trace the three boundaries

ToolCall holds the requested name and arguments. ExecutableTool associates a name with a Pydantic parameter class and a handler. Dispatcher.invoke looks up the name, checks the allowlist, validates arguments and invokes the handler. The order matters: a refusal returned after the handler runs cannot undo a side effect. For consequential operations, the authority callback belongs before the handler. Our draft-only shop tools do not send purchases.

The construction exercise asks for the complete factory, not three constant answers. It must work with a changed shop, empty input and duplicate identities. The transfer adds reservations: sellable stock becomes physical stock minus reserved stock. Both reporting and draft validation must use the same revised rule, or the interface can contradict itself.

Choose an explicit starting point for this independent notebook

This Unit B runs without Unit A. By default it prepares a supplied reference starting point and labels its provenance. It is not evidence that you built Unit A. To investigate your own successful implementation, set LEARNER_HANDOFF to its saved path before running the cell. An invalid selected file refuses; it is never silently replaced with the reference.

SourceTask supplies copied-source execution and handoff validation; RuntimeLab supplies the controlled failure experiment. Their public operations are introduced beside the main exercise. The artifact stores identity and observations; no variables from another kernel are required.

LEARNER_HANDOFF = None

Prepare and validate the supplied starting artifact

import json
import runpy
import shutil
import textwrap
from pathlib import Path

COURSE_INPUT = COURSE_WORK / "ch02-unit-a-handoff-v1.json"
if LEARNER_HANDOFF is not None:
    learner_input = Path(LEARNER_HANDOFF).expanduser().resolve()
    if not learner_input.is_file():
        raise FileNotFoundError("The selected learner handoff does not exist")
    if learner_input != COURSE_INPUT.resolve():
        shutil.copy2(learner_input, COURSE_INPUT)
    HANDOFF_ORIGIN = "LEARNER_SELECTED"
else:
    source_task_class = runpy.run_path(
        str(COURSE_ROOT / "book/always_on/exercises/source_tasks_v1.py")
    )["SourceTask"]
    reference_task = source_task_class(COURSE_ROOT, 2)
    try:
        reference_task.install(textwrap.dedent(reference_task.fragment))
        reference_observation = reference_task.visible("SUPPLIED_REFERENCE_START")
        if reference_observation["status"] != "PASS":
            raise RuntimeError("The supplied starting point did not pass its connection check")
        reference_task.save(COURSE_INPUT, reference_observation)
    finally:
        reference_task.close()
    HANDOFF_ORIGIN = "SUPPLIED_REFERENCE"
print("Starting evidence:", HANDOFF_ORIGIN)
print("The core task below validates the selected artifact before using it.")

Understand the supplied execution interface

The course runtime is provided so your implementation can be connected to real callers and storage. SourceTask(ROOT, chapter) makes a private copy. install(source) replaces only the declared function; visible() invokes the real chapter probe; save(path, result) retains a successful implementation and its evidence. load(path) checks the saved identities and hashes. inject_failure() changes the declared boundary; repair(fragment) replaces that broken fragment. close() removes the scratch copy after you retain evidence. These methods are supplied harness operations, not additional packages you must discover or install.

RuntimeLab provides the same copied-source failure experiment without the complete-function construction layer. Its run method records exit status, observations and the compared expectation. A subprocess log from an unfinished learner implementation is feedback about that implementation; it is not a successful connection. A syntax error in the notebook cell itself is a separate issue to fix. The task below names which interface it uses.

For direct-function units, the visible driver calls your callback without installing a source string. In either case, trace where your code is invoked. Supplied fixtures, database wrappers and replay models are labelled infrastructure; your own implementation and changed-case explanation are the evidence of learning.

Main practical: construct, connect and challenge

An integer quantity can be schema-valid and still over-order. This time you begin with your Unit A implementation and its saved evidence. Lucy receives a seven-tub draft when stock requires six. She can over-order if she trusts that draft.

Verify the handoff

The starting-point cell has selected the Unit A artifact explicitly. A selected learner handoff must validate; the default reference start is labelled separately. Run the setup and keep the runtime and implementation hashes in your submission.

import json
import os
import runpy
from pathlib import Path

ROOT = COURSE_ROOT

SourceTask = runpy.run_path(str(ROOT / "book/always_on/exercises/source_tasks_v1.py"))["SourceTask"]
REFERENCE_LESSON = 2
HANDOFF = Path("ch02-unit-a-handoff-v1.json")
handoff_status = "MISSING"
if HANDOFF.is_file():
    task = SourceTask(ROOT, REFERENCE_LESSON)
    try:
        handoff = task.load(HANDOFF)
        handoff_status = "VERIFIED"
        print("IMPLEMENTATION", handoff["implementation_sha256"])
    finally:
        task.close()
print("UNIT_A_HANDOFF", handoff_status)

Reproduce and diagnose

Predict the consequence of this injected boundary before executing it:

if False:

The controlled mutation changes the same implementation you submitted. It refuses if the declared mutation boundary no longer occurs exactly once; inspect an alternative implementation with the instructor before adapting the experiment.

baseline = broken = None
if handoff_status == "VERIFIED":
    task = SourceTask(ROOT, REFERENCE_LESSON)
    try:
        task.load(HANDOFF)
        baseline = task.visible("YOUR_BASELINE")
        if baseline["status"] != "PASS":
            raise ValueError("Saved Unit A code no longer satisfies the visible contract")
        task.inject_failure()
        broken = task.run("INJECTED_FAILURE", expected=task.spec["expected_broken"])
        print("BEFORE", baseline["observation"])
        print("AFTER", broken["observation"])
    finally:
        task.close()
else:
    print("HANDOFF_REQUIRED: complete Unit A before performing Unit B")

State a diagnosis using those two observations. Name a test that would prove your diagnosis wrong. Compare schema refusal, business-rule refusal and successful calculation. Connect build_tools to the same Dispatcher used by the Chapter 3 loop.

Repair the boundary

Return the complete replacement for the injected fragment. Do not edit the oracle or print a desired observation. Repair the actual source. The starter keeps the defect so the learner outcome remains incomplete.

def repair_fragment():
    return "        if args.quantity != needed:"

Hint 1 — the consequence

Lucy receives a seven-tub draft when stock requires six. She can over-order if she trusts that draft.

Hint 2 — the evidence

Compare the two observations, then trace the changed field to build_tools in book/always_on/learner/ch02.py. Distinguish a schema refusal from a business-rule or authority refusal.

Hint 3 — the design

Use rows keyed by identity, closures over a deep copy, the existing argument models, and three ExecutableTool registrations.

def connect_repair(fragment):
    task = SourceTask(ROOT, REFERENCE_LESSON)
    try:
        task.load(HANDOFF)
        task.inject_failure()
        task.repair(fragment)
        return task.visible("YOUR_REPAIR")
    finally:
        task.close()


repair_result = None
if handoff_status == "VERIFIED":
    repair_result = connect_repair(repair_fragment())
    print("REPAIR", repair_result["status"], repair_result["observation"])
else:
    print("REPAIR_NOT_ATTEMPTED: missing Unit A evidence")

Transfer under a changed constraint

Try a changed stock snapshot, an empty catalog, duplicated identities and a True quantity. Record which boundary refuses each.

Create a fresh task, load your handoff, inject the defect and apply your repair. Then change only the copied probe to exercise the new condition. Keep the actual observation and a prediction written beforehand. Explain why a visible-case lookup or a blanket refusal could pass the original example but fail this transfer.

The instructor's holdout applies your repair to a new copied runtime and checks both the positive case and the missing protection. An exact exception or changed state must cause a failure; no broad error is accepted as successful refusal.

Exit ticket

Submit the original handoff, baseline and broken observations, repair, transfer probe and results. State what Lucy would experience before and after the fix. Identify the guarantee that still requires separate evidence: No supplier send exists in these tools.

passed = repair_result is not None and repair_result["status"] == "PASS"
exercise_report = {
    "unit": "ch02-b",
    "attempted": int(repair_result is not None),
    "completed": int(passed),
    "failed": int(repair_result is not None and not passed),
    "skipped": int(repair_result is None),
    "connection": "PASS" if passed else "NOT_READY",
    "handoff": handoff_status,
}
print("EXERCISE_REPORT=" + json.dumps(exercise_report, sort_keys=True))

Changed-constraint construction: Use one reservation rule in reporting and drafting

Allow twenty minutes. Spend three minutes predicting, ten implementing and tracing, five on a new case of your own, and two explaining the surviving limitation. This is dedicated work, not an invitation to run a supplied answer. Both units revisit the same invariant after different core experiences; in Unit B, attempt this task from memory before consulting Unit A.

Implement transfer_check(row). Return max(0, target - on_hand + reserved), treating an absent reserved field as zero. Inputs are validated nonnegative integers. The driver uses your result for both the stock report and the exact-quantity draft decision; changing your function must change both.

Write your expected values before running the table. Keep one accepted case and one refusal. Your function is passed directly into the driver below. The driver copies inputs and checks they remain unchanged; it does not replace your implementation with the reference answer.

Hint 1 — identify the authoritative inputs Name the source field for each output value. Which input changes while the rule remains the same?

Hint 2 — choose the boundary cases Start with exact empty, exact equality and one value on each side of the boundary where valid. Do not add a special case for a visible product name or operation identity.

def transfer_check(row):
    return max(0, row["target"] - row["on_hand"] + row.get("reserved", 0))

import copy
import json

TRANSFER_CASES = [
    ("reserved mango", [{"on_hand": 1, "target": 5, "reserved": 2}], 6),
    ("no reservations field", [{"on_hand": 1, "target": 5}], 4),
    ("surplus", [{"on_hand": 12, "target": 6, "reserved": 2}], 0),
    ("exact sellable target", [{"on_hand": 7, "target": 5, "reserved": 2}], 0),
]


def same_transfer_value(actual, expected):
    if type(actual) is not type(expected):
        return False
    if isinstance(expected, dict):
        return actual.keys() == expected.keys() and all(
            same_transfer_value(actual[key], value) for key, value in expected.items()
        )
    if isinstance(expected, list):
        return len(actual) == len(expected) and all(
            same_transfer_value(a, e) for a, e in zip(actual, expected, strict=True)
        )
    return actual == expected


def run_transfer(candidate, cases):
    observations = []
    for label, arguments, expected in cases:
        supplied = copy.deepcopy(arguments)
        before = copy.deepcopy(supplied)
        raised = None
        try:
            actual = candidate(*supplied)
        except NotImplementedError:
            raised = "NotImplementedError"
            actual = {"unfinished": True}
        except Exception as error:
            raised = type(error).__name__
            actual = {"raises": raised}
        expects_error = isinstance(expected, dict) and set(expected) == {"raises"}
        correct = (
            raised == expected["raises"]
            if expects_error
            else (raised is None and same_transfer_value(actual, expected))
        )
        passed = correct and same_transfer_value(supplied, before)
        observations.append(
            {"case": label, "expected": expected, "observed": actual, "passed": passed}
        )
        print("PASS" if passed else "NEEDS_WORK", label, "expected", expected, "observed", actual)
    return observations


transfer_observations = run_transfer(transfer_check, TRANSFER_CASES)
TRANSFER_PASSED = all(row["passed"] for row in transfer_observations)
print("TRANSFER_STATUS", "PASS" if TRANSFER_PASSED else "NEEDS_WORK")

Design a counterexample and retrieve the mechanism

Add one new case with an independently calculated expected outcome to TRANSFER_CASES and rerun the driver. Change one condition at a time. Then deliberately replace your candidate with a constant answer in a temporary copy and show a case that rejects it. Restore your implementation. Explain why that counterexample is stronger than repeating the original example with a new name.

Without viewing the worked example, write the invariant in words and trace one observed value back to its input. Identify which part is a local fixture result and which claim would need a live provider, host or external-system observation. Keep a first attempt even if you used a hint.

Connect the reservation rule to both callers

Predict the reported need and draft total. Both callers below invoke your function. A four-tub draft must refuse after two tubs are reserved.

if TRANSFER_PASSED:
    reservation_row = {"on_hand": 1, "target": 5, "reserved": 2}

    def reservation_stock():
        return {"needed": transfer_check(reservation_row)}

    def reservation_draft(quantity):
        if quantity != transfer_check(reservation_row):
            raise ValueError("quantity differs from current sellable-stock need")
        return {"quantity": quantity, "total_pence": quantity * 325, "status": "DRAFT"}

    assert reservation_stock()["needed"] == 6
    assert reservation_draft(6)["total_pence"] == 1950
    try:
        reservation_draft(4)
    except ValueError:
        print("Old four-tub request refused by the same shared rule.")
    else:
        raise AssertionError("The draft validator ignored the new rule")
    print(reservation_stock(), reservation_draft(6))
else:
    print("Finish the transfer function before connecting its two callers.")

Instructor explanation and additional transfer cases

Available stock is on_hand minus reserved. Substitute that expression into target minus available, then clamp at zero. Reusing the function prevents a report from saying six while a draft validator still demands four.

Ask for the learner's first prediction and attempt before revealing this version. Passing these cases verifies behavior on these inputs; it does not establish independent student mastery. The original core holdouts also run against the connected implementation below.

INSTRUCTOR_TRANSFER_CASES = [
    ("reservations exceed physical", [{"on_hand": 2, "target": 5, "reserved": 8}], 11),
    ("new boundary", [{"on_hand": 10, "target": 6, "reserved": 5}], 1),
]
instructor_transfer = run_transfer(transfer_check, INSTRUCTOR_TRANSFER_CASES)
assert TRANSFER_PASSED and all(row["passed"] for row in instructor_transfer)

# Instructor holdout appended to a submitted Chapter 2 Unit B.

# ruff: noqa: F821
import json

task = SourceTask(ROOT, REFERENCE_LESSON)
try:
    task.load(HANDOFF)
    task.inject_failure()
    task.repair(repair_fragment())
    outcome = task.transfer(ROOT / "book/always_on/exercises/ch02/holdouts/runtime-transfer-v1.py")
    assert outcome["status"] == "PASS", outcome
finally:
    task.close()
print("HOLDOUT_RESULT=" + json.dumps({"unit": "ch02-b", "status": "PASSED"}, sort_keys=True))

Save your evidence and explain the result

Fill the prediction notes and your explanation before saving. Include the exact observed value, the input or retained row that caused it, your code's invocation point, one failed hypothesis, and the strongest claim the evidence still cannot support. A completed code cell alone does not earn explanation credit. Do not label reference-start behavior as your own Unit A construction.

Keep this edited notebook, the Markdown if used for notes, saved handoff files, and the JSON record below. Your work folder survives scratch cleanup and can be reopened in a new kernel. An instructor can ask for an unseen case after the visible checks; keep your implementation general.

explanation_notes = {
    "causal_trace": "Explain the input, learner invocation and observed result.",
    "failed_hypothesis": "Describe a prediction the evidence changed.",
    "remaining_limit": "Name the guarantee not established by this experiment.",
}
course_submission = {
    "unit": "ch02-b",
    "planned_minutes": 90,
    "starting_evidence": globals().get("HANDOFF_ORIGIN", "INDEPENDENT_UNIT_A"),
    "prediction": prediction_notes,
    "explanation": explanation_notes,
    "core_report": exercise_report,
    "transfer": transfer_observations,
    "explanation_review": "HUMAN_REVIEW_REQUIRED",
}
submission_path = COURSE_WORK / "ch02-b-submission-v1.json"
submission_path.write_text(
    json.dumps(course_submission, indent=2, sort_keys=True), encoding="utf-8"
)
print("Saved evidence:", submission_path)
print(
    "COURSE_REPORT="
    + json.dumps(
        {
            "unit": "ch02-b",
            "transfer_passed": TRANSFER_PASSED,
            "starting_evidence": course_submission["starting_evidence"],
            "edition": "instructor",
        },
        sort_keys=True,
    )
)

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