SageMaker Core TrainingJob Incorrectly expects a resource Tag instead of shape Tag

[benieric]

Describe the bug
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When creating a TrainingJob and passing in the a Tag shape get an error like:

ValidationError: 1 validation error for create
tags.0
  Input should be a valid dictionary or instance of Tag [type=model_type, input_value=Tag(key='key', 
value='value'), input_type=Tag]
    For further information visit https://errors.pydantic.dev/2.9/v/model_type

Example of failing code:

from sagemaker.modules import Session
from sagemaker_core.resources import TrainingJob
from sagemaker_core.shapes import AlgorithmSpecification, ResourceConfig, StoppingCondition, Tag, OutputDataConfig

import time

session = Session()
training_job_name = f"base-name-{int(time.time())}"
role = session.get_caller_identity_arn()
output_bucket = session.default_bucket()


training_job = TrainingJob.create(
    training_job_name=training_job_name,
    role_arn=role,
    algorithm_specification=AlgorithmSpecification(
        training_image=image,
        training_input_mode="File",
    ),
    resource_config=ResourceConfig(
        instance_count=1,
        instance_type="ml.m5.large",
        volume_size_in_gb=30,
    ),
    output_data_config=OutputDataConfig(
        s3_output_path=f"s3://{output_bucket}/output",
    ),
    stopping_condition=StoppingCondition(
        max_runtime_in_seconds=86400,
    ),
    tags=[Tag(key="key", value="value")],

This is because there are duplicate class names for Tag in the shape module and resource module.

• Resource Tag - https://github.com/aws/sagemaker-core/blob/1918e57dce20ad44aff0ceb8b73e31ce497ff94e/src/sagemaker_core/main/resources.py#L27596
• Shape Tag -

  sagemaker-core/src/sagemaker_core/main/shapes.py

  Line 497 in 1918e57

  class Tag(Base):

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Expected behavior
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Bug information
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• SageMaker Core version:
• Python version:
• Is the issue with autogen code or with generate code ?:

Additional context
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[MattFriedman]

In ModelTrainer pydantic is demanding one version of Tag and when you call model_trainer.train() it is expecting a different version of Tag. So if you are using ModelTrainer and use [Tag] it will break.

Maintainers need to pick a consistent version of Tag and stick with it.

There are at least 2 versions of Tag in the codebase.