Sasctl

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**Improvements**
- Introduced `generate_model_card` into `write_json_files.py` to allow for python models to work with planned model card tab in SAS Model Manager.

**Bugfixes**
- Allow for score code to impute NaN values in tables that have been loaded into SAS Model Manager.
- Fix issue where target_value was not being properly set during score code generation
- Updated `pzmm_generate_requrirements_json.ipynb` so the requirements file is generated properly.
- Added missing statistics to `dmcas_fitstat.json` file.

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**Improvements**
- Introduced ability to specify the target index of a binary model when creating score code.
- index can be specified in `pzmm.import_model.ImportModel.import_model()`
- Relevant examples updated to include target_index.

**Bugfixes**
- Reworked `write_score_code.py` to allow for proper execution of single line scoring.
- Added template files for `assess_model_bias.py` to allow for proper execution

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**Improvements**
- `write_score_code.py` refactored to include ability to run batch scoring.
- Added handling for TensorFlow Keras models.
- Updated project creation to automatically set project properties based on contained models.
- Included capability to assess biases of a model using CAS FairAITools using `pzmm.write_json_files.assess_model_bias()`.
- Added custom KPI support for H2O, statsmodels, TensorFlow, and xgboost.
- Updated examples:
- Added example walking through the creation process of a simple TensorFlow Keras model.
- Added example detailing the usage of `pzmm.write_json_files.assess_model_bias()` for a simple regression model
- Updated `pzmm_custom_kpi_model_parameters` notebook to have correct parameter casing.

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**Improvements**
- Created pytest fixture to begin running Jupyter notebooks within the GitHub automated test actions.
- Updated examples:
- Custom KPI and model parameters example now checks for the performance job's status.
- Update H2O example to show model being published and scored using the "maslocal" destination.
- Updated models to be more realistic for `pzmm_binary_classification_model_import.ipynb`.

**Bugfixes**
- Adjust `pzmm.ScoreCode.write_score_code()` function to be compatible with future versions of pandas.
- Reworked H2O section of `pzmm.ScoreCode.write_score_code()` to properly call H2OFrame values.
- Fixed call to `pzmm.JSONFiles.calculate_model_statistics()` in `pzmm_binary_classification_model_import.ipynb`.

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**Improvements**
- Refactored gitIntegration.py to `git_integration.py` and added unit tests for better test coverage.

**Bugfixes**
- Fixed issue with ROC and Lift charts not properly being written to disk.
- Fixed JSON conversion for Lift charts that caused TRAIN and TEST charts to be incorrect.
- Fixed issue with H2O score code and number of curly brackets.
- Updated score code logic for H2O to account for incompatibility with Path objects.
- Fixed issue where inputVar.json could supply invalid values to SAS Model Manager upon model import.
- Fixed issue with `services.model_publish.list_models`, which was using an older API format that is not valid in SAS Viya 3.5 or SAS Viya 4.

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**Improvements**
- Add recursive folder creation and an example.
- Add example for migrating models from SAS Viya 3.5 to SAS Viya 4.

**Bugfixes**
- Fixed improper json encoding for `pzmm_h2o_model_import.ipynb` example.
- Set urllib3 < 2.0.0 to allow requests to update their dependencies.
- Set pandas >= 0.24.0 to include df.to_list alias for df.tolist.
- Fix minor errors in h2o score code generation