Label Models

Table of Contents

1. Overview
2. Model Attributes
3. Exceptions
4. Model Validators
5. Usage Examples

Overview

The Label models provide a structured way to manage label resources in Palo Alto Networks' Strata Cloud Manager. These models represent simple key-value labels used for resource classification and organization. The models handle validation of inputs and outputs when interacting with the SCM API.

Models

The module provides the following Pydantic models:

• LabelBaseModel: Base model with fields common to all label operations
• LabelCreateModel: Model for creating new labels
• LabelUpdateModel: Model for updating existing labels
• LabelResponseModel: Response model for label operations

All models use extra="forbid" configuration, which rejects any fields not explicitly defined in the model.

Model Attributes

LabelBaseModel

Attribute	Type	Required	Default	Description
name	str	Yes	None	Name of label. Max length: 63 chars
description	str	No	None	Description of the label

LabelCreateModel

Inherits all fields from LabelBaseModel without additional fields.

LabelUpdateModel

Extends LabelBaseModel by adding:

Attribute	Type	Required	Default	Description
id	UUID	Yes	None	The unique identifier of the label

LabelResponseModel

Extends LabelBaseModel by adding:

Attribute	Type	Required	Default	Description
id	UUID	Yes	None	The unique identifier of the label

Exceptions

The Label models can raise the following exceptions during validation:

• ValueError: Raised when field validation fails
• ValidationError: Raised by Pydantic when model validation fails

Model Validators

The Label models include validators to ensure data integrity:

• Name validation: Ensures the name meets the maximum length requirement of 63 characters
• ID validation: Ensures valid UUID format for update and response models

Usage Examples

Creating a Label

from scm.client import ScmClient

# Initialize client
client = ScmClient(
    client_id="your_client_id",
    client_secret="your_client_secret",
    tsg_id="your_tsg_id"
)

# Using dictionary
label_data = {
    "name": "environment",
    "description": "Environment classification label"
}

response = client.label.create(label_data)
print(f"Created label: {response.name} with ID: {response.id}")

Updating a Label

from scm.client import ScmClient

# Initialize client
client = ScmClient(
    client_id="your_client_id",
    client_secret="your_client_secret",
    tsg_id="your_tsg_id"
)

# Fetch existing label
existing = client.label.fetch(name="environment")

# Modify attributes using dot notation
existing.description = "Updated environment classification label"

# Pass modified object to update()
updated = client.label.update(existing)
print(f"Updated label: {updated.name}")

Listing Labels

from scm.client import ScmClient

# Initialize client
client = ScmClient(
    client_id="your_client_id",
    client_secret="your_client_secret",
    tsg_id="your_tsg_id"
)

# List all labels
all_labels = client.label.list()

for label in all_labels:
    print(f"Label: {label.name}")
    print(f"ID: {label.id}")
    print(f"Description: {label.description}")

Fetching a Label by Name

from scm.client import ScmClient

# Initialize client
client = ScmClient(
    client_id="your_client_id",
    client_secret="your_client_secret",
    tsg_id="your_tsg_id"
)

# Fetch label by name
label = client.label.fetch(name="environment")
if label:
    print(f"Found label: {label.name}")
    print(f"ID: {label.id}")
    print(f"Description: {label.description}")
else:
    print("Label not found")