Fixes docs building for the `orbit.envs` submodule (#438)

# Description

With recent upgrade to Python 3.10, the linter was forcing changing of
types to use the new syntax that uses "|" operator.

This means that the line in `base_env.py` was getting converted to this
type as well:

```python
# old
VecEnvObs = Dict[str, torch.Tensor | Dict[str, torch.Tensor]]

# new
VecEnvObs = dict[str, torch.Tensor | dict[str, torch.Tensor]]
```

Unfortunately, Pytorch does not support this type of alias yet. This
leads to errors when building the documentation. The MR reverts to the
old setting and disables `pyupgrade` linter check for that file
(currently not possible to disable the linter for a line itself).

Fixes #432

## Type of change

- Bug fix (non-breaking change which fixes an issue)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./orbit.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [x] I have run all the tests with `./orbit.sh --test` and they pass
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

.pre-commit-config.yaml

@@ -35,6 +35,8 @@ repos:
     hooks:
       - id: pyupgrade
         args: ["--py310-plus"]
+        # FIXME: This is a hack because Pytorch does not like: torch.Tensor | dict aliasing
+        exclude: "source/extensions/omni.isaac.orbit/omni/isaac/orbit/envs/base_env.py"
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:

docs/source/setup/developer.rst

@@ -232,8 +232,11 @@ class.
 The ``source/standalone`` directory contains various standalone applications designed using the extensions
 provided by ``orbit``. These applications are written in python and are structured as follows:
 
-* **demo**: Contains various demo applications that showcase the core framework ``omni.isaac.orbit``.
+* **demos**: Contains various demo applications that showcase the core framework ``omni.isaac.orbit``.
 * **environments**: Contains applications for running environments defined in ``omni.isaac.orbit_tasks`` with different agents.
   These include a random policy, zero-action policy, teleoperation or scripted state machines.
+* **tools**: Contains applications for using the tools provided by the framework. These include converting assets, generating
+  datasets, etc.
+* **tutorials**: Contains step-by-step tutorials for using the APIs provided by the framework.
 * **workflows**: Contains applications for using environments with various learning-based frameworks. These include different
   reinforcement learning or imitation learning libraries.

source/extensions/omni.isaac.orbit/omni/isaac/orbit/envs/base_env.py

@@ -8,7 +8,7 @@ from __future__ import annotations
 import builtins
 import torch
 from collections.abc import Sequence
-from typing import Any
+from typing import Any, Dict
 
 import omni.isaac.core.utils.torch as torch_utils
 
@@ -20,7 +20,7 @@ from omni.isaac.orbit.utils.timer import Timer
 from .base_env_cfg import BaseEnvCfg
 from .ui import ViewportCameraController
 
-VecEnvObs = dict[str, torch.Tensor | dict[str, torch.Tensor]]
+VecEnvObs = Dict[str, torch.Tensor | Dict[str, torch.Tensor]]
 """Observation returned by the environment.
 
 The observations are stored in a dictionary. The keys are the group to which the observations belong.

source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/data_collector/robomimic_data_collector.py

@@ -46,7 +46,7 @@ class RobomimicDataCollector:
         filename: str = "test",
         num_demos: int = 1,
         flush_freq: int = 1,
-        env_config: dict = None,
+        env_config: dict | None = None,
     ):
         """Initializes the data collection wrapper.
 
@@ -183,7 +183,7 @@ class RobomimicDataCollector:
                 # add data to key
                 self._dataset[f"env_{i}"][sub_keys[0]].append(value[i])
 
-    def flush(self, env_ids: Iterable[int] = (0)):
+    def flush(self, env_ids: Iterable[int] = (0,)):
         """Flush the episode data based on environment indices.
 
         Args: