New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

Currently assert_eq takes both check_dtypes & check_dtype. The latter is forwarded to assert_series_equal. For example see this code:

https://github.com/dask/

Bug/Feature Request Description

In [1]: import featuretools as ft                                                                                                                             

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)                                                                                                

In [3]: import pandas as pd    

Building the doc fails for example 40_advanced/example_single_configurations on the current development branch

Logs here

...
generating gallery for examples/40_advanced... [ 50%] example_debug_logging.py

Warning, treated as error:
/home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_single_configu

Describe the bug
when axis has duplicate value , onnxruntime compute result is all same value ,which is different with expect of tensorflow

Urgency
2020.11.18

System information
Linux Ubuntu 16.04

• ONNX Runtime installed from binary
• ONNX Runtime version:1.4.0
• Python version:3.5

Expected behavior
When there are duplicate values, the duplicate can be removed. j

Is your feature request related to a problem? Please describe.
EnsembleForecaster does not currently accept weights. With #1136 and #1139 being worked on it would be nice to optionally allow users to pass weights to EnsembleForecaster.

This would give users an out-of-the-box way to use ainverse error based weighting strategy in forecasting ensembles. Users could also supply other types

Hi! I was a bit surprised by the name of CVSplit. The name suggests that it is responsible for cross-validation, but the documentation reveals that it only trains and validates on one split. That doesn't match my understanding of cross-validation. It could [technically](https://en.m.wikipedia.org/wiki/Cross-va

Yes

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

Description

I'm the creator and only maintainer of the project at the moment. I'm working on adding new features and thus I would like to let this issue open for newcomers who want to contribute to the project.

Basically, I wrote the cli using argparse since it is part of the standard language already. However, I'm starting to rethin

What's wrong?