jax

Feature request

Is the addition of the 'OPTforSequenceClassification' class scheduled?
Is someone handling it?
When adding these functions, I wonder if it is possible to PR one by one, or if I have to PR all classes supported by other models.

Motivation

Added function of OPT class, which is being actively discussed recently

Your contribution

I personally use the forSequenceCla

It would be great if you could add the JAX equivalent of scipy.stats.mode, which is currently unavailable.

A use case may be a ML classification task with ensembles, where multiple models give different predictions, and we are interested in finding the most common one.

As an example, consider predictions to be a two-dimensional DeviceArray of predictions, with
shape = (number of model

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

Love the book! Ran into this display issue when viewing it on Chrome's PDF viewer:

Right now, qml.operation.expand_matrix is often called in a code-block like:

if wire_order is None or self.wires == Wires(wire_order):
    return canonical_matrix

expand_matrix(canonical_matrix, wires=self.wires, wire_order=wire_order)

see [pennylane/operation.py Line 587](https://github.com/PennyLaneAI/pennylane/blob/b6fc5380abea6215661704ebe2f5cb8e7a599635/pennylane/operation.p

A quick search for mixture distributions in numpyro only turns up examples using Categorical in conjunction with an array of distributions. Since sampling from discrete distributions is not always desirable, I have implemented a quick general purpose mixture distribution with continuous log probability.

class Mixture(Distribution):
    arg_constraints = {}

    def __init__(self

Dear Brax team,

Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?

From the top of my head I would do something like this:
Let's say the task is to es

these are the examples on docs/notebooks/

Probably it's necessary to add this line when running on TPUs https://github.com/google/jax#pip-installation-colab-tpu

I think this would be a good first issue