Label Models

Overview

The Label models provide a structured way to manage labels in Palo Alto Networks' Strata Cloud Manager. These models handle validation of inputs and outputs when interacting with the SCM API.

Attributes

Attribute	Type	Required	Default	Description
name	str	Yes	None	Name of the label. Max length: 63 chars.
description	str	No	None	Description of the label
id	UUID	Yes*	None	UUID of the label (required only for update and response models)

* Only required for update and response models

Exceptions

The Label models can raise the following exceptions during validation:

• ValueError: Raised when validation fails for any field, such as missing required fields or invalid formats

Model Validators

The Label models utilize Pydantic validators to ensure data integrity:

• Name validation ensures the name meets length requirements
• Description validation ensures proper formatting if provided
• ID validation for update and response models

Usage Examples

Creating a Label Object

# Using dictionary
from scm.config.setup import Label

label_dict = {
    "name": "environment",
    "description": "Environment classification label"
}

label_service = Label(api_client)
response = label_service.create(label_dict)

# Using model directly
from scm.models.setup import LabelCreateModel

label_obj = LabelCreateModel(
    name="environment",
    description="Environment classification label"
)

payload = label_obj.model_dump(exclude_unset=True)
response = label_service.create(payload)

Updating a Label

# Using dictionary
update_dict = {
    "id": "123e4567-e89b-12d3-a456-426655440000",
    "name": "environment-updated",
    "description": "Updated environment classification label"
}

response = label_service.update(update_dict)

# Using model directly
from scm.models.setup import LabelUpdateModel

update_label = LabelUpdateModel(
    id="123e4567-e89b-12d3-a456-426655440000",
    name="environment-updated",
    description="Updated environment classification label"
)

payload = update_label.model_dump(exclude_unset=True)
response = label_service.update(payload)

Model Hierarchy

The Label models follow a hierarchical structure:

• LabelBaseModel: Base model containing common attributes shared across all label models
• LabelCreateModel: Extends base model for label creation operations
• LabelUpdateModel: Extends base model with required ID field for updates
• LabelResponseModel: Extends base model with fields specific to API responses

Working with Label Models

LabelBaseModel

The base model defines common attributes needed for all label operations:

from scm.models.setup import LabelBaseModel

base = LabelBaseModel(
    name="environment",
    description="Environment label"
)

LabelCreateModel

Used when creating new labels:

from scm.models.setup import LabelCreateModel

create_model = LabelCreateModel(
    name="environment",
    description="Environment label"
)

# Convert to dictionary for API request
payload = create_model.model_dump(exclude_unset=True)

LabelUpdateModel

Used when updating existing labels:

from scm.models.setup import LabelUpdateModel

update_model = LabelUpdateModel(
    id="123e4567-e89b-12d3-a456-426655440000",
    name="environment",
    description="Updated environment label"
)

# Convert to dictionary for API request
payload = update_model.model_dump(exclude_unset=True)

LabelResponseModel

Represents labels returned from the API:

from scm.models.setup import LabelResponseModel

# Typically constructed from API response
response_model = LabelResponseModel(
    id="123e4567-e89b-12d3-a456-426655440000",
    name="environment",
    description="Environment label"
)

# Access attributes
print(f"Label ID: {response_model.id}")
print(f"Label Name: {response_model.name}")
print(f"Label Description: {response_model.description}")

Best Practices

1. Validation: Leverage Pydantic model validation by using the models directly rather than raw dictionaries when possible.

2. Type Checking: Use type hints and the models' typing support to catch errors early in development.

3. Excluding Unset Values: When converting models to dictionaries, use model_dump(exclude_unset=True) to only include fields that have been explicitly set.

4. Error Handling: Wrap model creation in try/except blocks to catch and handle validation errors gracefully.

5. Response Parsing: Use the LabelResponseModel to parse API responses and access attributes in a type-safe manner.