Expand README with JAX integration examples and dataclass usage (#28)

Fix pre-commit formatting issues and add comprehensive README tests

- Fixed README.md formatting with prettier (proper line wrapping)
- Removed trailing whitespace from README.md
- Fixed import error in __init__.py for missing _version module
- Added comprehensive test suite in tests/test_readme.py that validates:
  * Basic ImmutableMap examples from README
  * JAX integration patterns
  * Immutability and hashability properties
  * Python code block syntax validation
  * Demonstrates why mutable defaults fail in dataclasses

Signed-off-by: nstarman <nstarman@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: nstarman <8949649+nstarman@users.noreply.github.com>

Author: Copilot

README.md

@@ -61,6 +61,66 @@ print(ImmutableMap({"a": 1, "b": 2.0, "c": "3"}))
 # ImmutableMap({'a': 1, 'b': 2.0, 'c': '3'})
 ```
 
+### JAX Integration
+
+One of the key benefits of `ImmutableMap` is its compatibility with JAX. Since
+it's immutable and hashable, it can be used in places where JAX would normally
+complain about mutable objects like regular dictionaries.
+
+#### Using ImmutableMap as a Default in JAX Dataclasses
+
+Here's an example showing how `ImmutableMap` can be used as a default value in a
+dataclass, which is particularly useful with JAX:
+
+```python
+import functools
+import jax
+import jax.numpy as jnp
+from dataclasses import dataclass
+from xmmutablemap import ImmutableMap
+
+
+@functools.partial(
+    jax.tree_util.register_dataclass, data_fields=["params"], meta_fields=["batch_size"]
+)
+@dataclass(frozen=True)
+class Config:
+    """Configuration with immutable default parameters."""
+
+    # This works! ImmutableMap is immutable and hashable
+    params: ImmutableMap[str, float] = ImmutableMap(
+        learning_rate=0.001, momentum=0.9, weight_decay=1e-4
+    )
+    batch_size: int = 32
+
+
+# JAX can safely transform functions using this dataclass
+@jax.jit
+def train_step(config: Config, data: jnp.ndarray) -> jnp.ndarray:
+    """Example training step that uses config parameters."""
+    lr = config.params["learning_rate"]
+    return data * lr
+
+
+# This works perfectly
+config = Config()
+data = jnp.array([1.0, 2.0, 3.0])
+result = train_step(config, data)
+print(f"Result: {result}")
+# Result: [0.001 0.002 0.003]
+```
+
+#### Key Benefits for JAX
+
+- **Immutability**: Once created, `ImmutableMap` cannot be modified, preventing
+  accidental mutations that could break JAX's functional programming model
+- **Hashability**: JAX can safely cache and memoize functions that use
+  `ImmutableMap` instances
+- **PyTree Support**: `ImmutableMap` is registered as a JAX PyTree, so it works
+  seamlessly with JAX transformations like `jit`, `grad`, `vmap`, etc.
+- **Safe Defaults**: Can be used as default values in dataclasses without the
+  typical pitfalls of mutable defaults
+
 ## Development
 
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src/xmmutablemap/__init__.py

@@ -6,4 +6,8 @@
 __all__ = ["ImmutableMap", "__version__"]
 
 from ._core import ImmutableMap
-from ._version import version as __version__
+
+try:
+    from ._version import version as __version__
+except ImportError:
+    __version__ = "unknown"