semvec/pypi-packages huggingface dataset:
pymouser 0.8
Module to interact with Mouser's Search API
Requires Python: >=3.7

# PyMouser

## Installation

Install the package with pip.

```pip install --user pymouser```

## Usage:

```python
import pymouser


# Initialize the package with your API key
mouser = pymouser.MouserAPI('your-search-key')

# Search by Part-Number
err, res = mouser.search_by_PN('your-part-number')

# Check for errors or print the returned results
if err:
    print("Error during request:")
    print(err)
else:
    if res['NumberOfResult'] == 0:
        print("No results matched the part number")
    else:
        for match in res['Parts']:
            print("Match for PartNumber .... %s" % match['MouserPartNumber'])
            print("Description ............. %s" % match['Description'])
            print("Link to datasheet ....... %s" % match['DataSheetUrl'])
            print("Link to product page .... %s" % match['ProductDetailUrl'])
            print("")
```
supertransformerlib 2.4.0
A set of tools for performing ensemble based supertransformer
Requires Python: >=3.9
Keywords: torch,transformer,transformers

[![Python test then publish](https://github.com/smithblack-0/torch-supertransformerlib/actions/workflows/python-test-publish.yml/badge.svg?branch=master)](https://github.com/smithblack-0/torch-supertransformerlib/actions/workflows/python-test-publish.yml)


# What is this?

This is a collection of layers designed to achieve state-of-the-art results in the field of natural language processing.
It consists of a collection of layers, loss functions,
and utilities which either implement recent transformer discoveries,
or which have no analogue in the current NLP literature. It is built in torch.

The library primarily forcuses around working
with the word embeddings.

It has the following desirable properties when used correctly

* torchscript compatible
* O(N) with respect to words input (configured correctly)
* Ensembles natively supported.

## Documentation

See https://torch-supertransformerlib.readthedocs.io/en/latest/ for documentation