docs: use parentheses for callable names in prose (DOCS-1362) (#2327)

## Summary
Aligns English documentation with the convention that **functions and
methods are written with trailing `()`** when mentioned in prose (not
inside code samples), for example `wandb.init()` and `run.log()`.

## Changes
- Edits across **models**, **platform**, **release-notes**, **weave**
(English only; `ja/`, `ko/`, and `support/` were not included).
- **AGENTS.md**: adds a style bullet and links to the [Global Functions
overview](https://docs.wandb.ai/models/ref/python/functions).
- Small follow-ups: `wandb.controller()` link text, `wandb.restore()` in
a few historical release-note bullets, and backtick cleanup in ref prose
where `wandb.init()` appeared without code formatting.

## Issue
Resolves DOCS-1362

Made with [Cursor](https://cursor.com)

---------

Co-authored-by: Noah Luna <15202580+ngrayluna@users.noreply.github.com>
Co-authored-by: Dan Brian <dbrian@coreweave.com>

Author: 3 people

AGENTS.md

@@ -186,6 +186,7 @@ Use sentence case for all headings and page titles. Capitalize only the first wo
   - Directory paths: `runbooks/`
   - Commands in sentences: `git push`
   - Code elements: `wandb.init()`
+- **Callable names in prose**: When you refer to a Python function or method in running text (not inside a code sample), use parentheses with backticks, for example `wandb.init()` or `run.log()`. Module-level functions in the Python SDK are listed in the [Global Functions overview](/models/ref/python/functions).
 
 ### Code examples
 

models/artifacts/create-a-new-artifact-version.mdx

@@ -35,7 +35,7 @@ Based on your use case, select one of the tabs below to create a new artifact ve
 <Tab title="Inside a run">
 Create an artifact version within a W&B run:
 
-1. Create a run with `wandb.init`.
+1. Create a run with `wandb.init()`.
 2. Create a new artifact or retrieve an existing one with `wandb.Artifact`.
 3. Add files to the artifact with `.add_file`.
 4. Log the artifact to the run with `.log_artifact`.
@@ -108,7 +108,7 @@ with wandb.init() as run:
 
 #### Run 3
 
-Must run after Run 1 and Run 2 complete. The Run that calls `finish_artifact` can include files in the artifact, but does not need to.
+Must run after Run 1 and Run 2 complete. The Run that calls `wandb.Run.finish_artifact()` can include files in the artifact, but does not need to.
 
 ```python
 with wandb.init() as run:

models/artifacts/storage.mdx

@@ -11,7 +11,7 @@ During training, W&B locally saves logs, artifacts, and configuration files in t
 
 | File | Default location | To change default location set: |
 | ---- | ---------------- | ------------------------------- |
-| logs | `./wandb` | `dir` in `wandb.init` or set the `WANDB_DIR` environment variable |
+| logs | `./wandb` | `dir` in `wandb.init()` or set the `WANDB_DIR` environment variable |
 | artifacts | `~/.cache/wandb` | the `WANDB_CACHE_DIR` environment variable |
 | configs | `~/.config/wandb` | the `WANDB_CONFIG_DIR` environment variable |
 | staging artifacts for upload  | `~/.cache/wandb-data/` | the `WANDB_DATA_DIR` environment variable |

models/integrations/accelerate.mdx

@@ -26,7 +26,7 @@ accelerator.init_trackers(
 
 ...
 
-# Log to wandb by calling `accelerator.log`, `step` is optional
+# Log to wandb by calling accelerator.log(); step is optional
 accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=global_step)
 
 
@@ -40,8 +40,8 @@ Explaining more, you need to:
 - a project name via `project_name`
 - any parameters you want to pass to [`wandb.init()`](/models/ref/python/functions/init) via a nested dict to `init_kwargs`
 - any other experiment config information you want to log to your wandb run, via `config`
-3. Use the `.log` method to log to Weigths & Biases; the `step` argument is optional
-4. Call `.end_training` when finished training
+3. Use the `wandb.Run.log()` method to log to Weigths & Biases; the `step` argument is optional
+4. Call `.end_training()` when finished training
 
 ## Access the W&B tracker
 

models/integrations/add-wandb-to-any-library.mdx

@@ -144,11 +144,11 @@ with wandb.init(project="<project_name>", entity="<entity>") as run:
 W&B recommends that you use a context manager to ensure that your run is properly closed, even if an error occurs. If you do not use a context manager, you must call `run.finish()` to close the run and log all the data to W&B.
 
 <Tip>
-**When to call `wandb.init`**
+**When to call `wandb.init()`**
 
 Call `wandb.init()` as early as possible. W&B captures stdout, stderr, and error messages, which makes debugging easier.
 
-Wrap your entire training loop in a `wandb.init` context manager to ensure that all relevant information is captured in the run. This includes any error messages, which can be crucial for debugging.
+Wrap your entire training loop in a `wandb.init()` context manager to ensure that all relevant information is captured in the run. This includes any error messages, which can be crucial for debugging.
 </Tip>
 
 ### Set `wandb` as an optional dependency
@@ -170,7 +170,7 @@ python train.py ... --use-wandb
 </Tab>
 </Tabs>
 
-* Or, set `wandb` to be `disabled` in `wandb.init`:
+* Or, set `wandb` to be `disabled` in `wandb.init()`:
 
 <Tabs>
 <Tab title="Python">
@@ -292,7 +292,7 @@ See [`wandb.Run.log()`](/models/ref/python/experiments/run#method-run-log) for m
 
 If you perform multiple calls to `wandb.Run.log()` for the same training step, the wandb SDK increments an internal step counter for each call to `wandb.Run.log()`. This counter may not align with the training step in your training loop.
 
-To avoid this situation, define your x-axis step explicitly with `run.define_metric`, one time, immediately after you call `wandb.init`:
+To avoid this situation, define your x-axis step explicitly with `wandb.Run.define_metric()`, one time, immediately after you call `wandb.init()`:
 
 ```python
 with wandb.init(...) as run:

models/integrations/farama-gymnasium.mdx

@@ -3,7 +3,7 @@ description: "Integrate W&B with Farama Gymnasium to track reinforcement learnin
 title: Farama Gymnasium
 ---
 
-If you're using [Farama Gymnasium](https://gymnasium.farama.org/#) we will automatically log videos of your environment generated by `gymnasium.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`](/models/ref/python/functions/init) to `True`.
+If you're using [Farama Gymnasium](https://gymnasium.farama.org/#) we will automatically log videos of your environment generated by `gymnasium.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init()`](/models/ref/python/functions/init) to `True`.
 
 Our gymnasium integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/c5fe3d56b155655980611d32ef09df35cd336872/wandb/integration/gym/__init__.py#LL69C67-L69C67) being logged from `gymnasium` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video](/models/track/log/media/).
 

models/integrations/fastai.mdx

@@ -179,7 +179,7 @@ notebook_launcher(train, num_processes=2)
 
 ### Log only on the main process
 
-In the examples above, `wandb` launches one run per process. At the end of the training, you will end up with two runs. This can sometimes be confusing, and you may want to log only on the main process. To do so, you will have to detect in which process you are manually and avoid creating runs (calling `wandb.init` in all other processes)
+In the examples above, `wandb` launches one run per process. At the end of the training, you will end up with two runs. This can sometimes be confusing, and you may want to log only on the main process. To do so, you will have to detect in which process you are manually and avoid creating runs (calling `wandb.init()` in all other processes)
 
 <Tabs>
 <Tab title="Script">

models/integrations/huggingface_transformers.mdx

@@ -453,9 +453,9 @@ WANDB_SILENT=true
 </Tabs>
 
 
-### How do I customize `wandb.init`?
+### How do I customize `wandb.init()`?
 
-The `WandbCallback` that `Trainer` uses will call `wandb.init` under the hood when `Trainer` is initialized. You can alternatively set up your runs manually by calling `wandb.init` before the`Trainer` is initialized. This gives you full control over your W&B run configuration.
+The `WandbCallback` that `Trainer` uses will call `wandb.init()` under the hood when `Trainer` is initialized. You can alternatively set up your runs manually by calling `wandb.init()` before the`Trainer` is initialized. This gives you full control over your W&B run configuration.
 
 An example of what you might want to pass to `init` is below. For `wandb.init()` details, see the [`wandb.init()` reference](/models/ref/python/functions/init).
 

models/integrations/kubeflow-pipelines-kfp.mdx

@@ -147,9 +147,9 @@ Here's a mapping of Kubeflow Pipelines concepts to W&B
 
 ## Fine-grain logging
 
-If you want finer control of logging, you can sprinkle in `wandb.log` and `wandb.log_artifact` calls in the component.
+If you want finer control of logging, you can sprinkle in `wandb.log()` and `wandb.log_artifact()` calls in the component.
 
-### With explicit `wandb.log_artifacts` calls
+### With explicit `wandb.log_artifact()` calls
 
 In this example below, we are training a model. The `@wandb_log` decorator will automatically track the relevant inputs and outputs. If you want to log the training process, you can explicitly add that logging like so:
 

models/integrations/lightning.mdx

@@ -25,7 +25,7 @@ trainer = Trainer(logger=wandb_logger)
 ```
 
 <Note>
-**Using wandb.log():** The `WandbLogger` logs to W&B using the Trainer's `global_step`. If you make additional calls to `wandb.log` directly in your code, **do not** use the `step` argument in `wandb.log()`. 
+**Using wandb.log():** The `WandbLogger` logs to W&B using the Trainer's `global_step`. If you make additional calls to `wandb.log()` directly in your code, **do not** use the `step` argument in `wandb.log()`. 
 
 Instead, log the Trainer's `global_step` like your other metrics:
 
@@ -295,7 +295,7 @@ for epoch in range(num_epochs):
 
 Using wandb's [`define_metric`](/models/ref/python/experiments/run#define_metric) function you can define whether you'd like your W&B summary metric to display the min, max, mean or best value for that metric. If `define`_`metric` _ isn't used, then the last value logged with appear in your summary metrics. See the `define_metric` [reference docs here](/models/ref/python/experiments/run#define_metric) and the [guide here](/models/track/log/customize-logging-axes/) for more.
 
-To tell W&B to keep track of the max validation accuracy in the W&B summary metric, call `wandb.define_metric` only once, at the beginning of training:
+To tell W&B to keep track of the max validation accuracy in the W&B summary metric, call `wandb.define_metric()` only once, at the beginning of training:
 
 <Tabs>
 <Tab title="PyTorch Logger">
@@ -396,7 +396,7 @@ Here you can organize your best models by task, manage model lifecycle, facilita
 
 The `WandbLogger` has `log_image`, `log_text` and `log_table` methods for logging media.
 
-You can also directly call `wandb.log` or `trainer.logger.experiment.log` to log other media types such as Audio, Molecules, Point Clouds, 3D Objects and more.
+You can also directly call `wandb.log()` or `trainer.logger.experiment.log()` to log other media types such as Audio, Molecules, Point Clouds, 3D Objects and more.
 
 <Tabs>
 <Tab title="Log Images">