feat: Add a functional API for optimization (#98)

* feat: Add a functional API for optimization

This commit introduces an alternative API for
EvoTorch that conforms to the functional
programming paradigm. This functional API can
be used together with `torch.func.vmap`, and
therefore can be used for optimizing not just
a single population, but a batch of populations
simultaneously.

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Co-authored-by: Rupesh K Srivastava <rupesh@nnaisense.com>

examples/notebooks/Functional_API/README.md

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+# Functional API for Optimization
+
+As an alternative to its object-oriented stateful API, EvoTorch provides an API that conforms to the functional programming paradigm. This functional API has its own advantages like being able to work on not just a single population, but on a batch of populations.
+
+Here are the examples demonstrating various features of this functional API:
+
+- **[Maintaining a batch of populations using the functional EvoTorch API](batched_searches.ipynb)**: This notebook shows how one can efficiently run multiple searches simultaneously, each with its own population and hyperparameter configuration, by maintaining a batch of populations.
+- **[Solving constrained optimization problems](constrained.ipynb)**: EvoTorch provides batching-friendly constraint penalization functions that can be used with both the object-oriented API and the functional API. In addition, these constraint penalization functions can be used with gradient-based optimization. This notebook demonstrates these features.
+- **[Solving reinforcement learning tasks using functional evolutionary algorithms](problem.ipynb)**: The functional evolutionary algorithm implementations of EvoTorch can be used to solve problems that are expressed using the object-oriented core API of EvoTorch. To demonstrate this, this notebook instantiates a `GymNE` problem for the reinforcement learning task "CartPole-v1", and solves it using the functional `pgpe` implementation.