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...Git Cola enables additional features when the following Python modules are installed. send2trash enables cross-platform "Send to Trash" functionality. Never run pip install or make install as root or outside of a Python virtualenv! If you don't have PyQt installed then the easiest way to get it is to use a Python virtualenv and install Git Cola into it in "editable" mode. This install method lets you upgrade Git Cola by running git pull.

django-braces provides useful Mixins for Django's class-based views. Most of these mixins replicate the behavior of Django's function-based view decorators. Others solve common headaches with working with class-based views. django-braces is stable and time-tested. It does not receive a lot of updates and is not in active development. django-braces also only officially supports Python versions that are still receiving fixes and Django LTS versions. django-braces will work with a most modern...

...Schemas for the device capabilities of each device. The preferred way to get Amazon Braket Python Schemas is by installing the Amazon Braket Python SDK, which will pull in the schemas. You can install from source by cloning this repository and running a pip install command in the root directory of the repository. There are currently two types of IR, including jaqcd (JsonAwsQuantumCircuitDescription) and annealing.

The Amazon Braket Ocean Plugin is an open-source library in Python that provides a framework that you can use to interact with Ocean tools on top of Amazon Braket. Before you begin working with the Amazon Braket Ocean Plugin, make sure that you've installed or configured the following prerequisites. Download and install Python 3.7.2 or greater from Python.org. If you are using Windows, choose Add Python to environment variables before you begin the installation. Make sure that your AWS account is onboarded to Amazon Braket. The Amazon Braket Ocean Plugin can be installed with pip. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. ...

...SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. In order to use the I/O functionalies in the sagemaker_sklearn_extension.externals module, you will also need to install the mlio version 0.7 package via conda. The mlio package is only available through conda at the moment. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. For unit tests, tox will use pytest to run the unit tests in a Python 3.7 interpreter. tox will also run flake8 and pylint for style checks.

...The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. Such parameter-space sparsity used for model compression compresses networks that operate on dense tensors and all intermediate activations of these networks are also dense tensors.