### Install Pharos

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/visualization.md

Install the Pharos plugin using pip.

```console
pip3 install pharos
```

--------------------------------

### Install TensorBay Python SDK

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/README.md

Install the TensorBay Python SDK using pip.

```console
pip3 install tensorbay
```

--------------------------------

### Verify TensorBay Installation

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/getting_started_with_tensorbay.md

Verify the installed SDK and CLI version by running this command in your terminal. This confirms a successful installation.

```console
gas --version
```

--------------------------------

### Example Profile Summary Output

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/advanced_features/profile.md

This is an example of the output format for a saved profile summary file. It includes details on API paths, total time, call numbers, average time, response length, and file size.

```text
|Path                    |totalTime (s) |callNumber  |avgTime (s) |totalResponseLength  |totalFileSize (B)|
|[GET] data06/labels     |11.239        |25          |0.450       |453482               |0                |
|[GET] data06/data/urls  |16.739        |25          |0.670       |794545               |0                |
|[POST] oss-cn-shanghai  |0.567         |10          |0.057       |0                    |8058707          |
```

--------------------------------

### Initialize GAS Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/search/basic_search.md

Initializes the GAS client with your AccessKey and gets a dataset client. Replace '<YOUR_ACCESSKEY>' and '<DATASET_NAME>' with your actual credentials and dataset name.

```python
from tensorbay import GAS

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.get_dataset("<DATASET_NAME>")

```

--------------------------------

### Word Object Example

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_sentence.md

Demonstrates the creation and serialization of a Word object.

```python
>>> word = Word(text="example", begin=1, end=2)
>>> word.dumps()
{'text': 'example', 'begin': 1, 'end': 2}
```

--------------------------------

### Example of CLI Completion

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/completion.md

Demonstrates how shell completion suggests command and option names when using the `gas` CLI. Options are shown after a dash is entered.

```console
$ gas <TAB><TAB>
auth     -- Authenticate the accessKey of gas.
branch   -- List, create or delete branches.
commit   -- Commit drafts.
config   -- Configure the options when using gas CLI.
cp       -- Copy local data to a remote path.
dataset  -- List, create or delete datasets.
draft    -- List or create drafts.
log      -- Show commit logs.
ls       -- List data under the path.
rm       -- Remove the remote data.
tag      -- List, create or delete tags.
$ gas auth -<TAB><TAB>
--get    -g  -- Get the accesskey of the profile
--status -s  -- Get the user info and accesskey of the profile
--unset  -u  -- Unset the accesskey of the profile
--all    -a  -- All the auth info
--help       -- Show this message and exit.
```

--------------------------------

### Creating a LabeledBox3D Instance

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/Box3D.md

Example of how to create a LabeledBox3D object using its constructor with size, translation, rotation, category, attributes, and instance.

```APIDOC
## Creating a LabeledBox3D Instance

```pycon
>>> from tensorbay.label import LabeledBox3D
>>> box3d_label = LabeledBox3D(
... size=[10, 20, 30],
... translation=[0, 0, 0],
... rotation=[1, 0, 0, 0],
... category="<LABEL_CATEGORY>",
... attributes={"<LABEL_ATTRIBUTE_NAME>": "<LABEL_ATTRIBUTE_VALUE>"},
... instance="<LABEL_INSTANCE_ID>"
... )
>>> box3d_label
LabeledBox3D(
  (size): Vector3D(10, 20, 30),
  (translation): Vector3D(0, 0, 0),
  (rotation): quaternion(1.0, 0.0, 0.0, 0.0),
  (category): '<LABEL_CATEGORY>',
  (attributes): {...},
  (instance): '<LABEL_INSTANCE_ID>'
)
```
```

--------------------------------

### Access a Dataset Segment

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/THCHS30.md

Get a specific segment from the dataset by its name. For example, access the 'dev' segment.

```python
segment = dataset["dev"]
```

--------------------------------

### Initialize and Configure SentenceSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/Sentence.md

Demonstrates how to initialize a SentenceSubcatalog and set its properties like is_sample, sample_rate, and lexicon.

```python
>>> from tensorbay.label import SentenceSubcatalog
>>> sentence_subcatalog = SentenceSubcatalog()
>>> sentence_subcatalog.is_sample = True
>>> sentence_subcatalog.sample_rate = 5
>>> sentence_subcatalog.append_lexicon(["text", "spell", "phone"])
>>> sentence_subcatalog
SentenceSubcatalog(
  (is_sample): True,
  (sample_rate): 5,
  (lexicon): [...]
)
```

--------------------------------

### Access a Dataset Segment

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/DogsVsCats.md

Get a specific segment from the dataset by its name. For example, accessing the 'train' segment.

```python
segment = dataset["train"]
```

--------------------------------

### Show All TensorBay Configurations

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Display all current configuration settings for TensorBay.

```bash
$ gas config
```

--------------------------------

### Initialize TensorBay GAS Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/draft_and_commit.md

Initialize the GAS client with your AccessKey and create a dataset client. Visit the provided URL to obtain your AccessKey.

```python
from tensorbay import GAS

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.create_dataset("<DATASET_NAME>")
```

--------------------------------

### Initialize and Add Keypoints to Keypoints2DSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/Keypoints2D.md

Demonstrates initializing a Keypoints2DSubcatalog and adding keypoints with their associated information like names, skeleton, visibility, and parent categories. This can be done during initialization or after.

```python
from tensorbay.label import Keypoints2DSubcatalog
keypoints2d_subcatalog = Keypoints2DSubcatalog()
keypoints2d_subcatalog.add_keypoints(
3,
names=["head", "body", "feet"],
skeleton=[[0, 1], [1, 2]],
visible="BINARY",
parent_categories=["cat"],
description="keypoints of cats"
)
keypoints2d_subcatalog.keypoints

```

```python
[KeypointsInfo(
   (number): 3,
   (names): [...],
   (skeleton): [...],
   (visible): 'BINARY',
   (parent_categories): [...] 
)]
```

--------------------------------

### TBRN Example

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/glossary.md

An example of a TBRN for an image file within a specific dataset and segment. TBRNs are used in the CLI.

```default
tb:example:train://000000.jpg
```

--------------------------------

### Get TensorBay Authentication Information

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Retrieve the current authentication information for your TensorBay account. Use the --all flag to get information for all configurations.

```bash
$ gas auth --get [--all]
```

--------------------------------

### Get Remote Dataset with TensorBay SDK

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/advanced_features/cache.md

Get a remote dataset from TensorBay using your AccessKey and dataset name. Ensure you have your AccessKey from the Graviti platform.

```python
from tensorbay import GAS
from tensorbay.dataset import Dataset

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset = Dataset("<DATASET_NAME>", gas)
```

--------------------------------

### Get the top-left and bottom-right points of a Box2D

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/box.md

Access the `tl` and `br` properties to get Vector2D objects representing the top-left and bottom-right corners of the box, respectively.

```python
>>> box2d = Box2D(1, 2, 3, 4)
>>> box2d.tl
Vector2D(1, 2)
```

```python
>>> box2d = Box2D(1, 2, 3, 4)
>>> box2d.br
Vector2D(3, 4)
```

--------------------------------

### Authorize a Client Instance

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/CADC.md

Create a GAS client instance. Obtain your AccessKey from the TensorBay developer portal.

```python
from tensorbay import GAS
from tensorbay.dataset import FusionDataset

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
```

--------------------------------

### Get Latest Basic Search Job

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/search/basic_search.md

Retrieves the latest basic search job using its ID. Alternatively, `list_jobs()` can be used to get a list of all jobs, and the first element can be accessed.

```python
job = dataset_client.basic_search.get_job("<JOB_ID>")

```

```python
job = dataset_client.basic_search.list_jobs()[0]

```

--------------------------------

### Authorize a Sextant Instance

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/applications/sextant.md

Instantiate the Sextant client using your AccessKey. Visit the developer portal to obtain your AccessKey.

```python
from tensorbay.apps.sextant import Sextant

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
sextant = Sextant("<YOUR_ACCESSKEY>")
```

--------------------------------

### Initialize and Configure Polyline2DSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/Polyline2D.md

Demonstrates how to initialize a Polyline2DSubcatalog and set its properties after initialization. Use this when you need to customize the subcatalog's behavior, such as enabling Bezier curves.

```python
>>> from tensorbay.label import Polyline2DSubcatalog
>>> polyline2d_subcatalog = Polyline2DSubcatalog()
>>> polyline2d_subcatalog.is_beizer_curve = True
>>> polyline2d_subcatalog
Polyline2DSubcatalog(
  (is_beizer_curve): True,
  (is_tracking): False
)
```

--------------------------------

### Get Dataset Label Statistics

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/get_label_statistics.md

Instantiate the GAS client, get a dataset client, and then call get_label_statistics() to retrieve the statistics. The statistics object contains detailed information about label quantities and categories.

```python
>>> from tensorbay import GAS
>>> gas = GAS("YOUR_ACCESSKEY")
>>> dataset_client = gas.get_dataset("<DATASET_NAME>")
>>> statistics = dataset_client.get_label_statistics()
>>> statistics
Statistics {
    'BOX2D': {...},
    'BOX3D': {...},
    'KEYPOINTS2D': {...}
}
```

--------------------------------

### Initialize TensorBay Dataset Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/tag.md

Before operating on tags, you need to initialize a dataset client instance. Ensure you have your AccessKey and dataset name ready.

```python
from tensorbay import GAS

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.create_dataset("<DATASET_NAME>")
dataset_client.create_draft("draft-1")
# do the modifications in this draft
```

--------------------------------

### Transform3D.rotation

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/transform.md

Gets the rotation component of the 3D transform.

```APIDOC
#### property rotation *: quaternion*

### Description
Return the rotation of the 3D transform.

### Returns
Rotation in numpy quaternion.

### Examples
```pycon
>>> transform = Transform3D(matrix=[[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]])
>>> transform.rotation
quaternion(1, -0, -0, -0)
```
```

--------------------------------

### Transform3D.translation

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/transform.md

Gets the translation component of the 3D transform.

```APIDOC
#### property translation *: [Vector3D](vector.md#tensorbay.geometry.vector.Vector3D)*

### Description
Return the translation of the 3D transform.

### Returns
Translation in [`Vector3D`](vector.md#tensorbay.geometry.vector.Vector3D).

### Examples
```pycon
>>> transform = Transform3D(matrix=[[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]])
>>> transform.translation
Vector3D(1, 1, 1)
```
```

--------------------------------

### Initialize Dataset Client and Create Branches

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/squash_and_merge.md

Initializes a dataset client and creates multiple branches with commits. This sets up the environment for squash and merge operations.

```python
from tensorbay import GAS

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.create_dataset("<DATASET_NAME>")

dataset_client.create_draft("draft-1")
dataset_client.commit("commit-1")

dataset_client.create_branch("dev")
dataset_client.create_draft("draft-2")
dataset_client.commit("commit-2")

dataset_client.create_draft("draft-3")
dataset_client.commit("commit-3")

dataset_client.checkout("main")
dataset_client.create_draft("draft-4")
dataset_client.commit("commit-4")
```

--------------------------------

### Pipeline.copy

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/healthcheck/pipeline.md

Method to get a shallow copy of the pipeline.

```APIDOC
## copy()

### Description
Copy method to get a shallow copy of pipeline.

### Parameters
#### Path Parameters
- **self** ( _S*) - Required - 

### Returns
- ** _S* - A shallow copy of the pipeline.
```

--------------------------------

### Get Tag

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/version.md

Retrieves a specific tag by its name.

```APIDOC
## get_tag(name)

### Description
Get the certain tag with the given name.

### Parameters
#### Path Parameters
- **name** (str) - Required - The required tag name.

### Returns
- **Tag** - The Tag instance with the given name.
```

--------------------------------

### Authorize a Sextant Instance

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/applications/sextant.md

Initializes a Sextant client instance using an AccessKey. The AccessKey can be obtained from the Graviti developer portal.

```APIDOC
## Authorize a Sextant Instance

### Description
Initializes a Sextant client instance using an AccessKey.

### Method
```python
Sextant("<YOUR_ACCESSKEY>")
```

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```python
from tensorbay.apps.sextant import Sextant

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
sextant = Sextant("<YOUR_ACCESSKEY>")
```

### Response
None
```

--------------------------------

### Generate Fish Activation Script (Click 8.x)

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/completion.md

Generates the Fish completion script and saves it to `~/.config/fish/completions/gas.fish`. This file will be automatically loaded by Fish.

```fish
_GAS_COMPLETE=fish_source gas > ~/.config/fish/completions/gas.fish
```

--------------------------------

### FusionDatasetClient.get_or_create_segment

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/dataset.md

Gets or creates a fusion segment with the specified name.

```APIDOC
## get_or_create_segment(name='default')

### Description
Get or create a fusion segment with the given name.

### Parameters
#### Parameters
**name** (*str*) – The name of the fusion segment.

### Returns
The created [`FusionSegmentClient`](segment.md#tensorbay.client.segment.FusionSegmentClient) with given name.

### Return type
[*FusionSegmentClient*](segment.md#tensorbay.client.segment.FusionSegmentClient)
```

--------------------------------

### Generate Fish Activation Script (Click 7.x)

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/completion.md

Generates the Fish completion script and saves it to `~/.config/fish/completions/gas-complete.fish`. This file will be automatically loaded by Fish.

```fish
_GAS_COMPLETE=source_fish gas > ~/.config/fish/completions/gas-complete.fish
```

--------------------------------

### Get Branch

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/branch.md

Retrieves information about a specific branch by its name.

```APIDOC
## Get Branch

```python
branch = dataset_client.get_branch("T123")
```
```

--------------------------------

### Initialize Keypoint2D from coordinates and visibility

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/keypoint.md

Initialize a Keypoint2D object with its x, y coordinates and visibility status.

```python
>>> Keypoint2D(1.0, 2.0, 0)
Keypoint2D(1.0, 2.0, 0)
```

--------------------------------

### Initialize SentenceSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_sentence.md

Initialize SentenceSubcatalog using its constructor. Requires specifying sample-related information and lexicon.

```python
>>> SentenceSubcatalog(True, 16000, [["mean", "m", "iy", "n"]])
SentenceSubcatalog(
  (is_sample): True,
  (sample_rate): 16000,
  (lexicon): [...]
)
```

--------------------------------

### Get Benchmark by Name

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/apps/sextant.md

Retrieve a specific benchmark instance by its name.

```APIDOC
## get_benchmark(name)

### Description
Get a benchmark instance by name.

### Parameters
- **name** (str) - Name of the Benchmark.

### Returns
The Benchmark instance with the given name.

### Raises
[**ResourceNotExistError**](../exception.md#tensorbay.exception.ResourceNotExistError) – When the required benchmark does not exist.

### Return type
[*Benchmark*](#tensorbay.apps.sextant.Benchmark)
```

--------------------------------

### Get Commit

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/draft_and_commit.md

Retrieves a specific commit using its commit ID.

```APIDOC
## Get Commit

```python
commit = dataset_client.get_commit(commit_id)
```
```

--------------------------------

### Initialize Dataset Object

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/DogsVsCats.md

Instantiate a Dataset object to interact with the 'DogsVsCats' dataset. Requires an authenticated gas client.

```python
dataset = Dataset("DogsVsCats", gas)
```

--------------------------------

### Initialize and Configure Camera

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/sensor/sensor.md

Initializes a Camera sensor, sets its extrinsic and intrinsic parameters, including translation, rotation, camera matrix, and distortion coefficients.

```python
>>> from tensorbay.geometry import Vector3D
>>> from numpy import quaternion
>>> camera = Camera('Camera1')
>>> translation = Vector3D(1, 2, 3)
>>> rotation = quaternion(1, 2, 3, 4)
>>> camera.set_extrinsics(translation=translation, rotation=rotation)
>>> camera.set_camera_matrix(fx=1.1, fy=1.1, cx=1.1, cy=1.1)
>>> camera.set_distortion_coefficients(p1=1.2, p2=1.2, k1=1.2, k2=1.2)
>>> camera
Camera("Camera1")(
    (extrinsics): Transform3D(
        (translation): Vector3D(1, 2, 3),
        (rotation): quaternion(1, 2, 3, 4)
    ),
    (intrinsics): CameraIntrinsics(
        (camera_matrix): CameraMatrix(
            (fx): 1.1,
            (fy): 1.1,
            (cx): 1.1,
            (cy): 1.1,
            (skew): 0
        ),
        (distortion_coefficients): DistortionCoefficients(
            (p1): 1.2,
            (p2): 1.2,
            (k1): 1.2,
            (k2): 1.2
        )
    )
)
```

--------------------------------

### Get Draft

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/draft_and_commit.md

Retrieves a specific draft using its draft number.

```APIDOC
## Get Draft

```python
draft = dataset_client.get_draft(draft_number=1)
```
```

--------------------------------

### Get Evaluation Result

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/applications/sextant.md

Fetches the detailed result of a specific evaluation.

```APIDOC
## Get Evaluation Result

### Description
Retrieves the detailed results of an evaluation.

### Method
`evaluation.get_result()`

### Endpoint
N/A (SDK method)

### Parameters
None

### Request Example
```python
result = evaluation.get_result()
```

### Response
Returns a dictionary containing the evaluation results, including category-specific and overall metrics.
```

--------------------------------

### Initialize empty RLESubcatalog and add attributes

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_polygon.md

Create an empty RLESubcatalog and then add categories and attributes. This approach is useful for dynamic catalog construction.

```python
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> rle_subcatalog = RLESubcatalog()
>>> rle_subcatalog.is_tracking = True
>>> rle_subcatalog.add_category("a")
>>> rle_subcatalog.add_attribute("gender", enum=["female", "male"])
>>> rle_subcatalog
RLESubcatalog(
  (is_tracking): True,
  (categories): NameList [...],
  (attributes): NameList [...] 
)
```

--------------------------------

### Get a Specific Tag

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/tag.md

Retrieve information about a specific tag by its name.

```python
tag = dataset_client.get_tag("Tag-1")
```

--------------------------------

### Initialize Dataset Client and Create Commits

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/branch.md

Before operating branches, a dataset client instance with existing commits is required. This snippet shows how to initialize the client and create initial commits for subsequent branch operations.

```python
from tensorbay import GAS

# Please visit `https://gas.graviti.com/tensorbay/developer` to get the AccessKey.
gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.create_dataset("<DATASET_NAME>")
dataset_client.create_draft("draft-1")
# Add some data to the dataset.
dataset_client.commit("commit-1", tag="V1")
commit_id_1 = dataset_client.status.commit_id

dataset_client.create_draft("draft-2")
# Do some modifications to the dataset.
dataset_client.commit("commit-2", tag="V2")
commit_id_2 = dataset_client.status.commit_id
```

--------------------------------

### Initialize and populate Keypoints2DSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_keypoints.md

Initialize an empty Keypoints2DSubcatalog and then programmatically add categories, attributes, and keypoints. This approach is suitable for building a catalog dynamically.

```python
from tensorbay.label import CategoryInfo, AttributeInfo, Keypoints2DSubcatalog, KeypointsInfo
from tensorbay.utility import NameList
categories = NameList()
categories.append(CategoryInfo("a"))
attributes = NameList()
attributes.append(AttributeInfo("gender", enum=["female", "male"]))
keypoints2d_subcatalog = Keypoints2DSubcatalog()
keypoints2d_subcatalog.is_tracking = True
keypoints2d_subcatalog.categories = categories
keypoints2d_subcatalog.attributes = attributes
keypoints2d_subcatalog.add_keypoints(
    2,
    names=["L_shoulder", "R_Shoulder"],
    skeleton=[(0,1)],
    visible="BINARY",
    parent_categories="shoulder",
    description="12345",
)
```

--------------------------------

### Get Rotation from Transform3D

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/transform.md

Access the rotation component of a Transform3D object as a quaternion.

```python
>>> transform = Transform3D(matrix=[[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]])
>>> transform.rotation
quaternion(1, -0, -0, -0)
```

--------------------------------

### Get Translation from Transform3D

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/geometry/transform.md

Access the translation component of a Transform3D object as a Vector3D.

```python
>>> transform = Transform3D(matrix=[[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]])
>>> transform.translation
Vector3D(1, 1, 1)
```

--------------------------------

### Initialize Box2DSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/CommonSubcatalogProperties.md

Instantiate a Box2DSubcatalog, optionally enabling tracking information support.

```python
>>> from tensorbay.label import Box2DSubcatalog
>>> box2d_subcatalog = Box2DSubcatalog(is_tracking=True)
>>> box2d_subcatalog
Box2DSubcatalog(
   (is_tracking): True
)
```

--------------------------------

### Get Tag

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/tag.md

Retrieve information about a specific tag associated with a dataset commit.

```APIDOC
## Get Tag

```python
tag = dataset_client.get_tag("Tag-1")
```
```

--------------------------------

### Initialize TensorBay Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/examples/move_and_copy.md

Initializes the TensorBay client using an access key and retrieves a dataset client. Ensure you have your access key and the dataset name.

```python
from tensorbay import GAS

gas = GAS("<YOUR_ACCESSKEY>")
dataset_client = gas.get_dataset("OxfordIIITPet")
dataset_client.list_segment_names()
# test, trainval
```

--------------------------------

### SentenceSubcatalog Initialization and Usage

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/Sentence.md

Demonstrates how to initialize SentenceSubcatalog and set its properties, including adding lexicon entries.

```APIDOC
## SentenceSubcatalog

Before adding sentence labels to the dataset, [`SentenceSubcatalog`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog) should be defined.

Besides [attributes information](CommonSubcatalogProperties.md#attributes-information) in
[`SentenceSubcatalog`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog),
it also has [`is_sample`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog.is_sample),
[`sample_rate`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog.sample_rate)
and [`lexicon`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog.lexicon).
to describe the transcripted sentences of the audio.

The catalog with only Sentence subcatalog is typically stored in a json file as follows:

```json
{
    "SENTENCE": {                                     <object>*
        "isSample":                                  <boolean>! -- Whether the unit of sampling points in Sentence label is the
                                                                   number of samples. The default value is false and the units
                                                                   are seconds.
        "sampleRate":                                 <number>  -- Audio sampling frequency whose unit is Hz. It is required
                                                                   when "isSample" is true.
        "description":                                <string>! -- Subcatalog description, (default: "").
        "attributes": [                                <array>  -- Attribute list, which contains all attribute information.
            {
                "name":                               <string>* -- Attribute name.
                "enum": [...],                         <array>  -- All possible options for the attribute.
                "type":                      <string or array>  -- Type of the attribute including "boolean", "integer",
                                                                   "number", "string", "array" and "null". And it is not
                                                                   required when "enum" is provided.
                "minimum":                            <number>  -- Minimum value of the attribute when type is "number".
                "maximum":                            <number>  -- Maximum value of the attribute when type is "number".
                "items": {                            <object>  -- Used only if the attribute type is "array".
                    "enum": [...],                     <array>  -- All possible options for elements in the attribute array.
                    "type":                  <string or array>  -- Type of elements in the attribute array.
                    "minimum":                        <number>  -- Minimum value of elements in the attribute array when type is
                                                                   "number".
                    "maximum":                        <number>  -- Maximum value of elements in the attribute array when type is
                                                                   "number".
                },
                "description":                        <string>! -- Attribute description, (default: "").
            },
            ...
            ...
        ]
        "lexicon": [                                   <array>  -- A list consists all of text and phone.
            [
                text,                                 <string>  -- Word.
                phone,                                <string>  -- Corresponding phonemes.
                phone,                                <string>  -- Corresponding phonemes (A word can correspond to more than
                                                                   one phoneme).
                ...
            ],
            ...
        ]
    }
}
```

#### NOTE
`*` indicates that the field is required. `!` indicates that the field has a default value.

Besides giving the parameters while initializing
[`SentenceSubcatalog`](../api/label/label_sentence.md#tensorbay.label.label_sentence.SentenceSubcatalog),
it’s also feasible to set them after initialization.

```python
>>> from tensorbay.label import SentenceSubcatalog
>>> sentence_subcatalog = SentenceSubcatalog()
>>> sentence_subcatalog.is_sample = True
>>> sentence_subcatalog.sample_rate = 5
>>> sentence_subcatalog.append_lexicon(["text", "spell", "phone"])
>>> sentence_subcatalog
SentenceSubcatalog(
  (is_sample): True,
  (sample_rate): 5,
  (lexicon): [...]
)
```

To add a [`LabeledSentence`](../api/label/label_sentence.md#tensorbay.label.label_sentence.LabeledSentence) label to one data:

```python
>>> from tensorbay.dataset import Data
>>> data = Data("<DATA_LOCAL_PATH>")
>>> data.label.sentence = []
>>> data.label.sentence.append(sentence_label)
```
```

--------------------------------

### Unset TensorBay Configuration

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Remove a specific configuration setting by its key, for example, 'editor'.

```bash
$ gas config --unset editor
```

--------------------------------

### Initialize and configure Polyline2DSubcatalog manually

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_polyline.md

Create an empty Polyline2DSubcatalog and then programmatically add categories and attributes. This method provides more control during initialization, especially when building the catalog structure dynamically.

```python
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> from tensorbay.utility import NameList
>>> categories = NameList()
>>> categories.append(CategoryInfo("a"))
>>> attributes = NameList()
>>> attributes.append(AttributeInfo("gender", enum=["female", "male"]))
>>> polyline2d_subcatalog = Polyline2DSubcatalog()
>>> polyline2d_subcatalog.is_tracking = True
>>> polyline2d_subcatalog.is_beizer_curve = True
>>> polyline2d_subcatalog.categories = categories
>>> polyline2d_subcatalog.attributes = attributes
>>> polyline2d_subcatalog
Polyline2DSubcatalog(
  (is_beizer_curve): True,
  (is_tracking): True,
  (categories): NameList [...],
  (attributes): NameList [...]
)
```

--------------------------------

### Set TensorBay Editor Configuration

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Set the editor to be used by TensorBay, for example, 'vim'.

```bash
$ gas config editor vim
```

--------------------------------

### Sextant.list_benchmarks()

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/apps/index.md

Lists all available benchmarks.

```APIDOC
## Sextant.list_benchmarks()

### Description
Lists all available benchmarks.

### Method
Not specified (likely a method call on a Sextant object)

### Endpoint
Not applicable (SDK method)

### Parameters
None explicitly documented.

### Request Example
```python
sextant.list_benchmarks()
```

### Response
- **benchmarks** (list[Benchmark]) - A list of Benchmark objects.
```

--------------------------------

### FusionSearchResult Operations

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/index.md

Operations for FusionSearchResult, including listing frames and getting sensor information.

```APIDOC
## FusionSearchResult.list_frames()

### Description
Lists the frames within the fusion search result.

### Method
(Not specified in source)

### Endpoint
(Not specified in source)

### Parameters
(Not specified in source)

### Request Example
(Not specified in source)

### Response
(Not specified in source)

## FusionSearchResult.get_sensors()

### Description
Gets the sensor information for the fusion search result.

### Method
(Not specified in source)

### Endpoint
(Not specified in source)

### Parameters
(Not specified in source)

### Request Example
(Not specified in source)

### Response
(Not specified in source)
```

--------------------------------

### Get Auth Storage Config

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/gas.md

Retrieves a specific authentication storage configuration by its name.

```APIDOC
## get_auth_storage_config(name)

### Description
Get the auth storage config with the given name.

### Parameters
* **name** (*str*) – The required auth storage config name.

### Returns
The auth storage config with the given name.
* **Return type:**
  [*StorageConfig*](cloud_storage.md#tensorbay.client.cloud_storage.StorageConfig)

### Raises
* **TypeError** – When the given auth storage config is illegal.
* [**ResourceNotExistError**](../exception.md#tensorbay.exception.ResourceNotExistError) – When the required auth storage config does not exist.
```

--------------------------------

### GAS Client Initialization

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/gas.md

Initializes the GAS client for interacting with TensorBay. Requires an access key and optionally a URL.

```APIDOC
## GAS(access_key, url='')

### Description
Initializes the GAS client for interacting between local and TensorBay.

### Parameters
* **access_key** (*str*) – User’s access key.
* **url** (*str*) – The host URL of the gas website.
```

--------------------------------

### Get User Info

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/gas.md

Retrieves user information associated with the current access key.

```APIDOC
## get_user()

### Description
Get the user information with the current accesskey.

### Returns
The [`struct.UserInfo`](struct.md#tensorbay.client.struct.UserInfo) with the current accesskey.
* **Return type:**
  [*UserInfo*](struct.md#tensorbay.client.struct.UserInfo)
```

--------------------------------

### Get a Specific Commit

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/draft_and_commit.md

Retrieve details of a specific commit using its commit ID.

```python
commit = dataset_client.get_commit(commit_id)
```

--------------------------------

### InstanceMaskSubcatalog Initialization

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_mask.md

Demonstrates two methods for initializing an InstanceMaskSubcatalog: from a dictionary using `loads()` and by adding categories and attributes programmatically.

```APIDOC
## InstanceMaskSubcatalog Initialization

### Description
Demonstrates two methods for initializing an InstanceMaskSubcatalog: from a dictionary using `loads()` and by adding categories and attributes programmatically.

### Initialization Method 1: Using `loads()`
```python
catalog = {
    "INSTANCE_MASK": {
        "categories": [
            {'name': 'background', "categoryId": 0}
        ],
        "attributes": [{'name': 'occluded', 'type': 'boolean'}]
    }
}
InstanceMaskSubcatalog.loads(catalog["INSTANCE_MASK"])
```

### Initialization Method 2: Programmatic Addition
```python
instance_mask_subcatalog = InstanceMaskSubcatalog()
instance_mask_subcatalog.add_category("background", 0)
instance_mask_subcatalog.add_attribute("occluded", type_="boolean")
```
```

--------------------------------

### Initialize and Populate PolygonSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_polygon.md

Shows how to create an empty PolygonSubcatalog and then programmatically add categories and attributes. This approach is suitable for dynamically building catalog structures.

```python
>>> from tensorbay.utility import NameList
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> categories = NameList()
>>> categories.append(CategoryInfo("a"))
>>> attributes = NameList()
>>> attributes.append(AttributeInfo("gender", enum=["female", "male"]))
>>> polygon_subcatalog = PolygonSubcatalog()
>>> polygon_subcatalog.is_tracking = True
>>> polygon_subcatalog.categories = categories
>>> polygon_subcatalog.attributes = attributes
>>> polygon_subcatalog
PolygonSubcatalog(
  (is_tracking): True,
  (categories): NameList [...],
  (attributes): NameList [...]
)
```

--------------------------------

### Get a Specific Draft

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/version_control/draft_and_commit.md

Retrieve details of a specific draft using its draft number.

```python
draft = dataset_client.get_draft(draft_number=1)
```

--------------------------------

### Show Specific TensorBay Configuration

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Display the value of a specific configuration key, for example, 'editor'.

```bash
$ gas config editor
```

--------------------------------

### List Configured Storage

Source: https://context7.com/graviti-ai/tensorbay-python-sdk/llms.txt

List all storage configurations that have been set up with TensorBay. This helps in managing and verifying storage settings.

```python
# List all configured storage configs
for config in gas.list_auth_storage_configs():
    print(config.name)
```

--------------------------------

### Get Cloud Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/gas.md

Retrieves a cloud client for interacting with a specified cloud storage configuration.

```APIDOC
## get_cloud_client(name)

### Description
Get a cloud client used for interacting with cloud platform.

### Parameters
* **name** (*str*) – The required auth storage config name.

### Returns
The cloud client of this dataset.
* **Return type:**
  [*CloudClient*](cloud_storage.md#tensorbay.client.cloud_storage.CloudClient)
```

--------------------------------

### List Benchmarks

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/apps/sextant.md

List all available benchmarks using the Sextant client.

```APIDOC
## list_benchmarks()

### Description
List all benchmarks.

### Returns
The list of Benchmark instances.

### Return type
[*PagingList*](../client/lazy.md#tensorbay.client.lazy.PagingList)[[*Benchmark*](#tensorbay.apps.sextant.Benchmark)]
```

--------------------------------

### Get BasicSearchJob Information

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/features/search/basic_search.md

Access various details of a BasicSearchJob, including its status, results, and arguments.

```APIDOC
## Get Information

Available BasicSearchJob information includes `title`, `description`, `job_id`, `arguments`, `created_at`, `started_at`, `finished_at`,
`status`, `error_message` and `result`.

```python
job.status
job.result
job.error_message
job.arguments
```

### Attributes

*   **title** (str) - The title of the search job.
*   **description** (str) - The description of the search job.
*   **job_id** (str) - The unique identifier for the search job.
*   **arguments** (dict) - The arguments used to create the search job.
*   **created_at** (str) - The timestamp when the job was created.
*   **started_at** (str) - The timestamp when the job started execution.
*   **finished_at** (str) - The timestamp when the job finished execution.
*   **status** (str) - The current status of the job (e.g., 'pending', 'running', 'completed', 'failed').
*   **error_message** (str) - If the job failed, this contains the error message.
*   **result** (SearchResult or FusionSearchResult) - The result of the search job. This will be a `SearchResult` or `FusionSearchResult` object if the job completed successfully.
```

--------------------------------

### List or Get Benchmark

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/applications/sextant.md

Retrieve a list of all available benchmarks or fetch a specific benchmark by its name.

```python
# list all benchmarks.
benchmarks = sextant.list_benchmarks()

# get benchmark with given name.
benchmark = sextant.get_benchmark("test_01")
```

--------------------------------

### Activate Fish Completion (Click 7.x)

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/completion.md

Place this command in `~/.config/fish/completions/gas.fish` to enable Fish tab completion for `gas` when using Click version 7.x.

```fish
eval (env _GAS_COMPLETE=source_fish gas)
```

--------------------------------

### Create OSS Storage Config

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/gas.md

Creates an authentication storage configuration for Alibaba Cloud OSS.

```APIDOC
## create_oss_storage_config(name, file_path, *, endpoint, accesskey_id, accesskey_secret, bucket_name)

### Description
Create an oss auth storage config.

### Parameters
* **name** (*str*) – The required auth storage config name.
* **file_path** (*str*) – dataset storage path of the bucket.
* **endpoint** (*str*) – endpoint of the oss.
* **accesskey_id** (*str*) – accesskey_id of the oss.
* **accesskey_secret** (*str*) – accesskey_secret of the oss.
* **bucket_name** (*str*) – bucket_name of the oss.

### Returns
The cloud client of this dataset.
* **Return type:**
  [*CloudClient*](cloud_storage.md#tensorbay.client.cloud_storage.CloudClient)
```

--------------------------------

### Show Commit Logs from Revision

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/tensorbay_cli/cli_commands.md

Retrieves commit logs starting from a specific revision of the dataset.

```bash
$ gas log tb:<dataset_name>@<revision>
```

--------------------------------

### Initialize and Configure MultiPolyline2DSubcatalog

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/label/label_polyline.md

Create an empty `MultiPolyline2DSubcatalog` and then programmatically add tracking information, categories, and attributes. This is useful for dynamically building subcatalog configurations.

```python
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> multi_polyline2d_subcatalog = MultiPolyline2DSubcatalog()
>>> multi_polyline2d_subcatalog.is_tracking = True
>>> multi_polyline2d_subcatalog.add_category(CategoryInfo("a"))
>>> multi_polyline2d_subcatalog.add_attribute(
    AttributeInfo("gender", enum=["female", "male"]))
>>> multi_polyline2d_subcatalog
MultiPolyline2DSubcatalog(
  (is_tracking): True,
  (categories): NameList [...],
  (attributes): NameList [...]
)
```

--------------------------------

### Creating a PanopticMask Label

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/label_format/PanopticMask.md

Example of how to create a PanopticMask label object in Python using the TensorBay SDK.

```APIDOC
## Create PanopticMask Label

```python
from tensorbay.label import PanopticMask

panoptic_mask_label = PanopticMask(local_path="</panoptic_mask/mask_image.png>")
panoptic_mask_label.all_category_ids = {1: 2, 2: 2}
print(panoptic_mask_label)
```

### NOTE
In `PanopticMask`, the key and value of `all_category_ids` are instance ID and category ID, respectively, both of which should be integers.
```

--------------------------------

### BasicSearchJob.result

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/client/job.md

Gets the result of the BasicSearchJob. The result can be a SearchResult or FusionSearchResult, or None if the job has not completed or produced a result.

```APIDOC
## property BasicSearchJob.result

### Description
Get the result of the BasicSearchJob.

### Returns
The search result of the BasicSearchJob.

### Return type
[*SearchResult*](search.md#tensorbay.client.search.SearchResult) | [*FusionSearchResult*](search.md#tensorbay.client.search.FusionSearchResult) | None
```

--------------------------------

### Sextant Client

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/reference/api/apps/sextant.md

Initialize the Sextant client to interact with the Graviti Marketplace's Sextant app.

```APIDOC
## Sextant

### Description
This class defines the Sextant client for interacting with the Graviti Marketplace.

### Parameters
- **access_key** (str) - User’s access key.
- **url** (str) - The URL of the graviti gas website. Defaults to ''.
```

--------------------------------

### Prepare and Upload Dataset

Source: https://github.com/graviti-ai/tensorbay-python-sdk/blob/main/docs/source/quick_start/getting_started_with_tensorbay.md

Organize local data into a TensorBay Dataset object, create segments, append data, and then upload it to TensorBay. Ensure to commit the changes for version control.

```python
from tensorbay.dataset import Data, Dataset

# Organize the local dataset by the "Dataset" class before uploading.
dataset = Dataset("<DATASET_NAME>")

# TensorBay uses "segment" to separate different parts in a dataset.
segment = dataset.create_segment()

segment.append(Data("0000001.jpg"))
segment.append(Data("0000002.jpg"))

dataset_client = gas.upload_dataset(dataset, jobs=8)

# TensorBay provides dataset version control feature, commit the uploaded data before using it.
dataset_client.commit("Initial commit")
```