Context

We would like to add torch::nn::functional::normalize to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.normalize.

Steps

• Add torch::nn::NormalizeOptions to torch/csrc/api/include/torch/nn/options/normalization.h (add this file if it doesn’t exist), which should include the following parameters (based on https://pytorch.

When attempting to add an ndarray of type int64 to an ndarray of type uint64, numpy throws a TypeError:

TypeError: Cannot cast ufunc add output from dtype('float64') to dtype('uint64') with casting rule 'same_kind'

The error message incorrectly identifies the type of the first array as float64, when it is actually int64.

Reproducing code example:

impo

The original issue dask/dask#1699 solves being able to repartition a bag into more partitions (useful after a flatten() for example).

However, the doc string wasn't updated as part of the PR - it says: "Coalesce bag into fewer partitions."

I would suggest updating it to "Change number of partitions of bag". Happy to do a PR for it.

A similar fix was applied to da

When loading my CSV or Excel file with a Date field Orange does not recognize or provide the ability to change the field type to DateTime. All date fields come in as Categorical data. I'm trying to do a time series prediction but without a the dates being recognized I'm unable to build out my prediction model. I've tried date in several different formats to no avail.

Please note that the for

Implement hypergeometric functions (https://docs.scipy.org/doc/scipy/reference/special.html#hypergeometric-functions).

In numpy we have this:

>>>np.unravel_index(0, [1,32,32,1])
(0, 0, 0, 0)

>>>np.unravel_index([0,1,2], [1,32,32,1])
(array([0, 0, 0]), array([0, 0, 0]), array([0, 1, 2]), array([0, 0, 0]))

But in xtensor we can only use unravel with one index. Also we'll get by this not an xarray of
dimensions, but this:

"xt::get_strides_t<std::vector<int, std::allocator>>"

Why is it

MCVE Code Sample

# Your code here
import numpy as np
import xarray as xr

data = np.zeros((10, 4))
example_xr = xr.DataArray(data, coords=[range(10), ["