feat: Add Nvidia e2e beginner notebook and tool calling notebook (#1964)

# What does this PR do?
This PR contains two sets of notebooks that serve as reference material
for developers getting started with Llama Stack using the NVIDIA
Provider. Developers should be able to execute these notebooks
end-to-end, pointing to their NeMo Microservices deployment.
1. `beginner_e2e/`: Notebook that walks through a beginner end-to-end
workflow that covers creating datasets, running inference, customizing
and evaluating models, and running safety checks.
2. `tool_calling/`: Notebook that is ported over from the [Data Flywheel
& Tool Calling
notebook](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/nemo/data-flywheel)
that is referenced in the NeMo Microservices docs. I updated the
notebook to use the Llama Stack client wherever possible, and added
relevant instructions.

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
- Both notebook folders contain READMEs with pre-requisites. To manually
test these notebooks, you'll need to have a deployment of the NeMo
Microservices Platform and update the `config.py` file with your
deployment's information.
- I've run through these notebooks manually end-to-end to verify each
step works.

[//]: # (## Documentation)

---------

Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>

docs/notebooks/nvidia/tool_calling/config.py

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# (Required) NeMo Microservices URLs
+NDS_URL = "http://data-store.test:3000" # Data Store
+NEMO_URL = "http://nemo.test:3000" # Customizer, Evaluator, Guardrails
+NIM_URL = "http://nim.test:3000" # NIM
+
+# (Required) Configure the base model. Must be one supported by the NeMo Customizer deployment!
+BASE_MODEL = "meta-llama/Llama-3.2-1B-Instruct"
+
+# (Required) Hugging Face Token
+HF_TOKEN = ""
+
+# (Optional) Modify if you've configured a NeMo Data Store token
+NDS_TOKEN = "token"
+
+# (Optional) Use a dedicated namespace and dataset name for tutorial assets
+NMS_NAMESPACE = "nvidia-tool-calling-tutorial"
+DATASET_NAME = "xlam-ft-dataset-1"
+
+# (Optional) Entity Store Project ID. Modify if you've created a project in Entity Store that you'd
+# like to associate with your Customized models.
+PROJECT_ID = ""
+# (Optional) Directory to save the Customized model.
+CUSTOMIZED_MODEL_DIR = "nvidia-tool-calling-tutorial/test-llama-stack@v1"