v1.5.2

• Drop Python 3.10 support
• Fix etree.spec_like, jax.ShapeDtypeStruct,... support
• Changed jax.tree_util keep paths (for better flatten repr)

v1.5.1

• Fix look_at compatibility with pytorch
• Add dca.vectorize_method compatibility for tf.nest/tf.data.

v1.5.0

• Add tf.nest/tf.data compatibility for DataclassArray.
• Fix import error due to etils

v1.4.2

• Add dca.concat method in addition to dca.stack.
• Now require Python 3.9 (drop 3.8 support)

v1.4.1

• Add torch==2.0.0 support

v1.4.0

• Add torch support!
• Add .cpu(), .cuda(), .to() methods to move the dataclass from
  devices when using torch.
• Breaking: @dataclass(frozen=True) is now automatically applied

v1.3.0

• Added: Support for static dca.DataclassArray (dataclasses with only
  static fields).

v1.2.1

• Fixed: Compatibility with edc.dataclass(auto_cast=True) (fix the 'type' object is not subscriptable error)

v1.2.0

• Changed: By default, dataclass_array do not cast and broadcast inputs
  anymore.
• Changed: dca.DataclassArray fields can be annotated with named axis (e.g.
  FloatArray['*shape h w 3']). Note that consistency across fields is not
  checked yet.
• Added: @dca.dataclass_array to customize the dca.DataclassArray params

v1.1.0

• Added: Array types can be imported directly from dataclass_array.typing
• Added: Syntax to specify the shape of the DataclassArray (e.g. MyRay['h w']).
• Fixed: Correctly forward non-init fields in .replace, tree_map,
  @dca.vectorize_method