docs: update imports to reflect current classes

docs/docs/writing-plugins/common-plugin-tasks/add-a-publisher.mdx

@@ -162,8 +162,8 @@ def rules():
 If your publisher has expensive packaging steps, you can use `check_skip_request` to preemptively skip packaging when you know the publish will be skipped:
 
 ```python
+from pants.core.goals.package import PackageFieldSet
 from pants.core.goals.publish import CheckSkipRequest, CheckSkipResult
-from pants.core.goals import package
 
 
 @dataclass(frozen=True)
@@ -174,7 +174,7 @@ class MyPublishFieldSet(PublishFieldSet):
     repositories: MyRepositoriesField
     skip_push: MySkipPushField
 
-    def check_skip_request(self, package_fs: package.PackageFieldSet) -> CheckSkipRequest[Self] | None:
+    def check_skip_request(self, package_fs: PackageFieldSet) -> CheckSkipRequest[Self] | None:
         """Return a request to check if we should skip packaging."""
         return MyPublishSkipRequest(publish_fs=self, package_fs=package_fs)
 ```

docs/docs/writing-plugins/common-plugin-tasks/add-codegen.mdx

@@ -54,12 +54,13 @@ Instead of users having to explicitly add this dependency every time, you can dy
 To inject dependencies:
 
 1. Subclass the `Dependencies` field. Register this subclass on your protocol target type.
-2. Define a subclass of `InjectDependenciesRequest` and set the class property `inject_for` to the `Dependencies` subclass defined in the previous step. Register this new class with a [`UnionRule`](../the-rules-api/union-rules-advanced.mdx) for `InjectDependenciesRequest`.
-3. Create a new rule that takes your new `InjectDependenciesRequest` subclass as a parameter and returns `InjectedDependencies`.
+2. Define a `FieldSet` subclass that includes the `Dependencies` subclass and any other fields needed for inference.
+3. Define a subclass of `InferDependenciesRequest` and set the class property `infer_from` to the `FieldSet` subclass defined in the previous step. Register this new class with a [`UnionRule`](../the-rules-api/union-rules-advanced.mdx) for `InferDependenciesRequest`.
+4. Create a new rule that takes your new `InferDependenciesRequest` subclass as a parameter and returns `InferredDependencies`.
 
 ```python
 from pants.engine.addresses import Address
-from pants.engine.target import Dependencies, InjectDependenciesRequest, InjectedDependencies
+from pants.engine.target import Dependencies, FieldSet, InferDependenciesRequest, InferredDependencies
 from pants.engine.rules import collect_rules, rule
 from pants.engine.unions import UnionRule
 
@@ -72,18 +73,24 @@ class ProtobufSourceTarget(Target):
     alias = "protobuf_source"
     core_fields = (*COMMON_TARGET_FIELDS, ProtobufDependencies, ProtobufSourceField)
 
-class InjectProtobufDependencies(InjectDependenciesRequest):
-    inject_for = ProtobufDependencies
+class ProtobufDependenciesInferenceFieldSet(FieldSet):
+    required_fields = (ProtobufDependencies,)
+    dependencies: ProtobufDependencies
+
+class InferProtobufDependencies(InferDependenciesRequest):
+    infer_from = ProtobufDependenciesInferenceFieldSet
 
 @rule
-async def inject_dependencies(_: InjectProtobufDependencies) -> InjectedDependencies:
+async def infer_protobuf_dependencies(
+    request: InferProtobufDependencies
+) -> InferredDependencies:
     address = Address("3rdparty/python", target_name="protobuf")
-    return InjectedDependencies([address])
+    return InferredDependencies([address])
 
 def rules():
     return [
         *collect_rules(),
-        UnionRule(InjectDependenciesRequest, InjectProtobufDependencies),
+        UnionRule(InferDependenciesRequest, InferProtobufDependencies),
     ]
 ```
 

docs/docs/writing-plugins/the-rules-api/testing-plugins.mdx

@@ -153,12 +153,12 @@ We can write this test:
 from pants.engine.addresses import Address
 from pants.engine.fs import EMPTY_DIGEST, Snapshot
 from pants.engine.internals.graph import hydrate_sources
-from pants.engine.target import HydratedSources, HydrateSourcesRequest, Target, Sources
+from pants.engine.target import HydratedSources, HydrateSourcesRequest, MultipleSourcesField, Target
 from pants.testutil.rule_runner import run_rule_with_mocks
 
 class MockTarget(Target):
     alias = "mock_target"
-    core_fields = (Sources,)
+    core_fields = (MultipleSourcesField,)
 
 
 def test_find_needle_in_haystack() -> None:
@@ -442,24 +442,22 @@ Given this rule signature for running the linter Bandit:
 
 ```python
 @rule
-async def bandit_lint(
-    request: BanditRequest, bandit: Bandit, python_setup: PythonSetup
-) -> LintResults:
+async def bandit_lint(batch: BanditRequest.Batch) -> LintResult:
     ...
 ```
 
 We can write a test like this:
 
 ```python
-from pants.core.goals.lint import LintResult, LintResults
+from pants.core.goals.lint import LintResult
 from pants.testutil.rule_runner import QueryRule, RuleRunner
 
 @pytest.fixture
 def rule_runner() -> RuleRunner:
     return RuleRunner(
         rules=[
             *bandit_rules(),
-            QueryRule(LintResults, [BanditRequest]),
+            QueryRule(LintResult, [BanditRequest.Batch]),
         ],
         target_types=[PythonSourceTarget]
     )
@@ -470,11 +468,11 @@ def test_bandit(rule_runner: RuleRunner) -> None:
     ...
 
     # Run Bandit rule.
-    bandit_request = BanditRequest(...)
-    lint_results = rule_runner.request(LintResults, [bandit_request])
+    bandit_batch = BanditRequest.Batch(...)
+    lint_result = rule_runner.request(LintResult, [bandit_batch])
 ```
 
-Note that our `@rule` takes 3 parameters, but we only explicitly included `BanditRequest` in the inputs. This is possible because the engine knows how to compute all [Subsystems](./options-and-subsystems.mdx) based on the initial input to the graph. See [Concepts](./concepts.mdx).
+Note that the lint rule now takes a `Batch` object as input. The engine knows how to compute all [Subsystems](./options-and-subsystems.mdx) based on the initial input to the graph. See [Concepts](./concepts.mdx).
 
 We are happy [to help](/community/members) figure out what rules to register, and what inputs to pass to `rule_runner.request()`. It can also help to [visualize the rule graph](./tips-and-debugging.mdx) when running your code in production. If you're missing an input that you need, the engine will error explaining that there is no way to compute your `OutputType`.
 

docs/docs/writing-plugins/the-rules-api/union-rules-advanced.mdx

@@ -33,12 +33,12 @@ class LintTargetsRequest(ABC):
 
 
 @dataclass(frozen=True)
-class LintResults:
+class LintResult:
     ...
 
 
 @rule(polymorphic=True)
-async def lint_target(req: LintTargetsRequest) -> LintResults:
+async def lint_target(req: LintTargetsRequest) -> LintResult:
     # If no implementation for the member type is found, this generic
     # implementation will be invoked. In this case that is not useful,
     # so we raise.
@@ -63,7 +63,7 @@ async def lint(targets: Targets, union_membership: UnionMembership) -> Lint:
 ```
 
 ```python title="pants-plugins/bash/shellcheck.py"
-from pants.core.goals.lint import LintResults, LintTargetsRequest
+from pants.core.goals.lint import LintResult, LintTargetsRequest
 from pants.engine.rules import collect_rules, rule
 
 
@@ -75,7 +75,7 @@ class ShellcheckRequest(LintTargetsRequest):
 
 
 @rule
-async def shellcheck_target(req: ShellcheckRequest) -> LintResults:
+async def shellcheck_target(req: ShellcheckRequest) -> LintResult:
     # At runtime, calls to the generic `lint_target()` on a
     # `ShellcheckRequest` will be dispatched here.
     ...

docs/docs/writing-plugins/the-target-api/creating-new-targets.mdx

@@ -70,7 +70,7 @@ For example, you might like how `pex_binary` behaves in general, but you have a
 Rather than subclassing the original target type, use this pattern:
 
 ```python
-from pants.backend.python.target_types import PexBinaryTarget, PexEntryPointField
+from pants.backend.python.target_types import PexBinary, PexEntryPointField
 from pants.engine.target import Target
 from pants.util.ordered_set import FrozenOrderedSet
 
@@ -81,12 +81,12 @@ class DjangoEntryPointField(PexEntryPointField):
 class DjangoManagePyTarget(Target):
    alias = "django_manage_py"
    core_fields = (
-       *(FrozenOrderedSet(PexBinaryTarget.core_fields) - {PexEntryPoint}),
+       *(FrozenOrderedSet(PexBinary.core_fields) - {PexEntryPointField}),
        DjangoEntryPointField,
    )
 ```
 
-In this example, we register all of the fields of `PexBinaryTarget`, except for the field `PexEntryPoint `. We instead register our custom field `DjangoEntryPointField `.
+In this example, we register all of the fields of `PexBinary`, except for the field `PexEntryPointField`. We instead register our custom field `DjangoEntryPointField`.
 :::
 
 ## Step 2: Register the target type in `register.py`