When designing a web page it's important to layout the content in a way that makes it quick and easy for a viewer to get the information they are looking for. Part of knowing how to layout the information is knowing where people tend to look at webpage the most (the main section and the top of the page, typically) and then shaping the text on the site to put the most important information in the most viewed areas.
When people look at a web page, they want to be able to quickly understand what the page is about and how to get the information they are looking for from it. The longer it takes them to figure out where the information they are looking for on the page is, the more frustrated they become with the website.
Most people never take the time to fully read though all the content on a webpage, but rather they scan across the page looking for key words and phrases are match their interest for being on the website. Because of that, the text on a page should be clear and simple and easy to scan, so the users can get what they are looking for quickly and easily.
How people interact with a website through the navigating is a huge factor in determining how good of an experience the website is providing. The main navigation from page to page should be consistent, and any links in the content sections should be clearly labeled and interest the user in continuing to navigate around the site.
People usually visit websites with a certain goal in mind, and they are usually not interested in anything else the website offers that isn't a part of that goal. So when view a webpage we block out anything that seems irrelevant and only focus on the areas that will help up achieve our goal.
When faces with a lot of options on a webpage, people usually just click the first one that look like it might satisfy their need, rather than weigh out multiple options. This is probably because people don't want to spend extra time on a webpage if they don't have to, and it's probably still quicker to click the "back" button if the first option was wrong than to continue to weigh multiple options.
Most people don't care to take the time and learn how to use things properly. Usually people will start using a new piece of technology they are able to muddle along and figure out a way to use it on their own that may or may not be the proper way, but as long as it's working for the user they generally wont care.
Designed objects typically follow certain conventions in order to make it easier for users from all different backgrounds to easily understand how the object works, and this is the same for websites as they also will see very a very diverse set of users. Some of the main conventions for web design are to keep the navigation in the same general area of a page and using familiar icons.
Visual hierarchies are very important for helping uses navigate the page and quickly scan the information to pick out what they are looking for. Some keys to creating good visual hierarchies for web pages are to keep the most important things in the most prominent positions on the page, visually link things that are similar (like listed items), and nest things visually to show that it is a part of a larger section.
Dividing the content on a page into sections helps a user scan the page easier and quickly decide which sections they should pay more attention to. And because users scan pages very quickly it is important that each section is clear and easy to understand at just a glance.
There are some standard ways that web designers separate out clickable links from basic text by changing the color, underlining it, or styling it to look like an actual button. Sometimes those things don't always look the best with the overall design, but it's still important to separate links form normal text in some way to prevent user frustration.
Too much visual noise on a webpage can make it much harder for users to navigate, or make their experience on the webpage more frustrating than it should be. Some types of visual noise are when there are too many elements on a page that are demanding the users attention (like lots of bright animated ads), disorganization with a page layout that makes it difficult to easily scan, and adding in too many unimportant elements that make it harder for the user to find what they are looking for.
I think it's safe to day that most people that visit amazon.com do so knowing exactly what they are looking for, and the rest of the people that visit at least have a general idea of what it is they way, like a new book. To help make it easy to find the links we need from the home page, the design really cuts down on any extra text. Almost every link on the page is described in one or two words. There are other larger elements like the center images and ads, but for the most part the links are easy enough to scan because they are located in easy to spot area, along the top of the page and along the bottom of the center ads.
The hierarchy isn't the most obvious though the design of most of the links on the page, probably because most options are equally useful to users, such as the "gift cards" and "today's deals" links. There are a few options from the homepage that are at a slightly larger font size and bolded, like the "your account" and "your lists" options, which are easily the most important links on the page so it makes sense that they stand out a bit more. However they are also located in an area of the page where one would expect the main navigation and account options to be which also shows they are higher up in the hierarchy more than them being bolded does.
Wikipedia is designed in a way that makes it very clear and easy to navigate from one page to another. The main way it does this is with each page having the exact same layout, which makes it easy to always find the main navigation menu on the left hand side. They also make it easy to navigate through an article by listing a "contents" menu under the main introduction section so users do not have to scroll though large pages of text to find the information they are looking for. Lastly, it's easy to find the links to other articles from the text because the links are displayed in a blue font so the users always know which words are clickable and which are not.
Being that Wikipedia pages are very minimal on design but heavy on text, it is important for it to be broken down into chunks. One of the better ways the Wikipedia designs their pages is that it usually has a short introduction section of text at the top of the article, and then there are more specific lists of information on the right, and the sections of the content listed below the info text. So for people looking for specific information, it is easy to know which specific area of the page they should check for exact information they are looking for.


text:
Allison Rapp is a New York City-based music and culture journalist. Her work has appeared in Brooklyn Magazine, City Limits and more. She tweets at @allisonrapp22
40 Years Ago: A Rejected Babys Song Becomes Journey's Signature Ballad
"Open Arms" eventually made its way to No. 2 on the charts, becoming a set list staple.
How Keith Richards' Broken Heart Inspired Hit Song 'Ruby Tuesday'
She was a 20-year-old British 'Vogue' model he met at a party thrown by the Rolling Stones' producer and manager.
Ronnie Spector Dies: Rockers React
Brian Wilson, Steven Van Zandt and others pay tribute to late icon.
Rolling Stones' 60th Anniversary Honored With Royal Mail Stamps
Band's decades-long career highlighted on new British postage.
When Bob Saget Crashed a Rock Concert
Fans at 2007 Guster show got more than they bargained for.
An all-star cast of rockers helped the Starman ring in his 50th birthday in 1997.
Carole King's Legendary 1973 Central Park Concert to Be Released
Third Man Vault Package features singer-songwriter's famous New York City performance.
Beatles' 'Rooftop Concert' Coming to IMAX Theaters
Footage and audio of legendary performance has been optimized for one-night-only showing.
New 'Beavis and Butt-Head' Movie Coming 'Soon'
"No exact date yet," show's creator says. "They need some time to get back in shape."
Doors, 'The Doors:' A Track-by-Track Guide
Band's debut LP laid the groundwork for brief but electrifying career.
Nirvana 'Nevermind' Cover Art Lawsuit Dismissed
Spencer Elden, who launched the lawsuit last year, has one last opportunity to refile.
Two 1976 hits may be tied to one another.

redpajama_set_name: RedPajamaCommonCrawl


text:
Christian Bale, Sam Worthington and Anton Yelchin star in the sci-fi action sequel Terminator Salvation. In 2018, Marcus Wright (Worthington), a half-human and half-machine, comes to John Connor (Bale) on the eve of a resistance attack on Skynet.
Studio: Warner Bros. Pictures
Genre: Action, Science Fiction, Sequel
Director: McG
Screenwriter: Michael Ferris, John Brancato
Cast: Christian Bale, Sam Worthington, Anton Yelchin, Helena Bonham Carter, Bryce Dallas Howard, Moon Bloodgood, Common, Michael Ironside
Running Time: 1 hr. 54 min.
MPAA Rating: PG-13 for intense sequences of sci-fi violence and action, and language.
In the highly anticipated new installment of "The Terminator" film franchise, set in post-apocalyptic 2018, Christian Bale stars as John Connor, the man fated to lead the human resistance against Skynet and its army of Terminators. But the future Connor was raised to believe in is altered in part by the appearance of Marcus Wright (Sam Worthington), a stranger whose last memory is of being on death row. Connor must decide whether Marcus has been sent from the future, or rescued from the past. As Skynet prepares its final onslaught, Connor and Marcus both embark on an odyssey that takes them into the heart of Skynet's operations, where they uncover the terrible secret behind the possible annihilation of mankind.
Post Tags: #Anton Yelchin#Bryce Dallas Howard#Christian Bale#Common#Helena Bonham Carter#McG#Post-Apocalyptic#Sam Worthington#Sci-Fi#Terminator#Terminator Salvation
X-Men Origins: Wolverine – Theatrical Trailer
[ad name="468×60-text-ad"] Watch the official theatrical trailer with Hugh Jackman reprising his role as the claw-wielding Wolverine. Leading up to the events of "X-Men," "X-Men Origins: Wolverine" tells the story…
Read More X-Men Origins: Wolverine – Theatrical TrailerContinue
Jason Sudeikis, Josh Gad and Danny McBride voice the animated comedy Angry Birds.
Read More Angry BirdsContinue
What To Expect When You're Expecting
[ad name="468×60-text-film-ad"] New What To Expect When You're Expecting Movie Trailer. There is no judging in 'Dudes Group'. Cameron Diaz, Chris Rock, Jennifer Lopez and Elizabeth Bank star in the…
Read More What To Expect When You're ExpectingContinue
Tyler Perry Presents Peeples
[ad name="468×60-text-film-ad"] Craig Robinson, Kerry Washington and David Alan Grier star in the comedy Tyler Perry Presents Peeples. See the first movie trailer, photos and movie posters below. After a…
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Jennifer's Body
[ad name="468×60-text-film-ad"] Megan Fox and Amanda Seyfried star in Diablo Cody's teen horror thriller Jennifer's Body. Watch the movie trailer below. A newly possessed high school cheerleader (Fox) turns into…
Read More Jennifer's BodyContinue
[ad name="468×60-text-film-ad"] Official Movie Poster Debuts for Girl Most Likely. Kristen Wiig, Annette Bening and Matt Dillon star in the indie dramedy Girl Most Likely. See the movie trailer and…
Read More Girl Most LikelyContinue


text:
Totally Tina
The Award-winning Tina Turner Spectacular
Come shake a tail feather with the UK's supreme Tina – Justine Riddoch – and her talented cast.
This breathtaking recreation of a live Tina Turner concert celebrates the golden anniversary of the Queen of Rock 'n' Roll's signature tune Proud Mary. Tina's no-holds-barred take of the song established a reputation for fearsome live performances – and those never-ending legs!
Early hits, including River Deep – Mountain High and Nutbush City Limits, were just a taste of what was to come. We Don't Need Another Hero, Simply the Best, What's Love Got to Do With It, I Don't Wanna Lose You and When the Heartache is Over propelled Tina to stadium concert stardom.
Supported by her dazzling dancing girls in sequins, feathers and diamonds, Justine recreates those famed live performances.
Group Rates 20% off 10+ tickets
The SUPREME Tina, she's got the looks, she's got the moves, but most of all, she's got THE voice.
National Tribute Awards
Grab a drink at the bar
Join us for a drink before the show and get ready for a great night out.
Our bars open an hour and a half before the show. We've got a great range of drink options for everyone.
Paul Chowdhry: Family Friendly Comedian (No Children)
Sun 4 Dec 2022 | Main auditorium
Following a complete sell-out 2021 tour, star of global smash hit 'Live Innit', Taskmaster & the first British-Asian stand-up to sell-out London's Wembley Arena comes to Peterborough!
Fairytale of New York
Wed 2 Nov 2022 | Main auditorium
Direct from the West End and after a phenomenal sell out debut tour Fairytale of New York is back with an even bigger production for 2022, it's the ultimate feel good Irish-inspired Christmas show.
Beautiful Trauma- The Ultimate Live P!nk Experience
Thu 16 Feb | Main auditorium
International, award winning P!nk impersonator Stacy Green has been crowned the UK's Best Multi Tribute Act, performing at venues across the UK and further for the past 11 years. This amazing act can be seen with a phenomenal band to create the excitement and majesty that makes P!nk the modern legend and inspiration she has come to be known as.


text:
Frente Democrático Nacional may refer to:

 National Democratic Front (Mexico)
 National Democratic Front (Peru)

redpajama_set_name: RedPajamaWikipedia


text:
Carrie Carroll
Position title: Senior Sustainability Leader
Email: carrie.carroll@wisc.edu
Carrie leads sustainability planning and implementation for the UW Population Health Institute. This includes integrating program-wide approaches and evaluation results and supporting national partner relationships. Previously Carrie directed the national Robert Wood Johnson Foundation (RWJF) Culture of Health Prize program at the University of Wisconsin Population Health Institute.
Carrie has more than 20 years of experience in philanthropy where she managed foundation investments in national nonprofit organizations and developed resources for health and human services and education organizations in the Seattle, Washington area.
Carrie currently serves on the board of directors for Communities Joined in Action. She was awarded a Rotary International Fellowship to Senegal and a German Marshall Fund American Marshall Memorial Fellowship. She is a founding board member of Youth in Focus, an award-winning organization that surrounds youth with high quality teachers and adult mentors.
Carrie holds a Masters of Public Administration degree from the University of Washington Evans School of Policy and Governance, and a Bachelor of Arts in Journalism with an emphasis in environmental studies from the University of Wisconsin – Madison.


The mason tap kit includes all the tools you need to make flavorful homemade infusions. Each kit includes one mason tap—a Handcrafted stainless steel pour spot that attaches to any regular-mouth mason jar—and three mason jars of varying sizes.
Bring a touch of vintage style to the party with Artland's Masonware Garden Terrace beverage dispenser and stand set with mason jar mugs for retro appeal.
Keep cooking essentials at your fingertips with Cole & Mason's set of 8 jars of premium herbs and spices that store on a sleek stainless steel rotating carousel.


text:
CARB Compliant Parts and E.O. Search
Executive Order D-126-37
ARB >
Executive Orders >
D-126-37
Executive order D-126-37 was approved by the California Air Resource Board on October 27, 2014 for emissions compliance and installation on vehicles in New York and California. This executive order approved parts marts manufactured by General Motors /.
View Executive Order Document [PDF]
Approval Details
This Executive Order approved the specified parts on on October 27, 2014.
As of Tuesday, January 21st, 2020 this Executive Order has not been overturned or superceeded.
Approved Parts
See PDF
This Executive Order may be listed as:
C.A.R.B.E.O. D-126-37
Executive Order 126-37 / D126-37
ARB # D-126-37
Executive Order No: D-126-37
C.A.R.B. No. D-126-37
Resolution D-126-37
For Free CARB Executive Order Status verification, email an image of the device Executive Order label as well as the Year/Make/Model and Test Group # of the vehicle to [email protected]
Download: Executive Order D-126-37 PDF
D-126-37 Document:
https://arb.parts/Executive-Order/D-126-37.pdf
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text:
Patient-oriented research refers to research that engages patients as partners, focuses on patient-identified priorities and improves patient outcomes. This research, conducted by multidisciplinary teams in partnership with stakeholders, aims to apply the knowledge generated to improve health care systems and practices.
MSFHR is actively supporting a number of provincial and national initiatives aimed at enhancing the way patient-oriented research is conducted. These investments will increase the responsiveness of BC's health research system to provincial priorities by establishing supportive structures and fostering connections between researchers and research users.
BC SUPPORT Unit
The BC SUPPORT Unit is a multi-partner organization led by CIHR and created to support, streamline and increase patient-oriented research throughout BC. MSFHR is a major financial contributor and a key collaborative partner.
BC Primary Health Care Research Network (BC-PHCRN)
The network's goal is to encourage, facilitate, and support collaborations between government, health authorities, health professionals, and researchers. MSFHR and CIHR have each committed $500,000 over five years to support the network's development and operations. The BC-PHCRN is one of 11 SPOR Primary and Integrated Health Care Innovations networks across Canada.
MSFHR public and patient engagement strategy
MSFHR's public and patient engagement strategy outlines the Foundation's vision to ensure public and patient perspectives are integrated into our work in a meaningful way to enable relevant, accessible and useable research that improves health outcomes and the health system in BC.
Primary and Integrated Health Care Innovation Network
The pan-Canadian SPOR Network in Primary and Integrated Health Care Innovations is a network of networks, including the BC-PHCRN, that builds on regional and national assets in community-based primary and integrated health care. MSFHR is pleased to offer matching funds to support the highest ranked BC applications that secure programmatic grants via the 2017 CIHR SPOR Network in Primary and Integrated Health Care Innovation.
SPOR Networks in Chronic Disease
These networks are funded by CIHR as part of the national SPOR initiative. Their primary objective is to translate existing and new health research into measureable improvements in patient outcomes and health care delivery. MSFHR has provided support of $1.5 million over five years to support the BC-based research activities of three networks: Can-SOLVE CKD, CHILD-BRIGHT, and the SPOR Network in Diabetes and its Related Complications.


Coming off a season filled with milestones, Adam Hadwin believes there is no reason he can't return to his winning ways early in 2018.
The Canadian golfer will play his second tournament of the year next week at the CareerBuilder Challenge in La Quinta, Calif., an event where he has found a lot of success. He finished runner-up there in 2017 on the heels of his record 13-under-par 59 during the third round, and was sixth in 2016.
"You can't go to a place where you played well and finished second at the previous year and not think that you have a chance to win the golf tournament, that's for sure," Hadwin said from his home in Phoenix.
Hadwin had a career-year in 2016-17, earning more than US$3.4 million, finishing 26th on the FedEx Cup standings and becoming Canada's top-ranked male golfer. He earned first PGA Tour victory, become just the eighth golfer in history to fire a sub-60 round on Tour and became just the third Canadian to ever play in the Presidents Cup.
The breakout season earned him a spot in all four of golf's major championships in 2018. As a result, his schedule this year will be built around the majors (a "good problem to have," he said) and may not include the two-tournament Texas swing in May he has done in the past.
He's never played the FedEx St. Jude Classic in Memphis, Tenn., but wants to add that event this year, the week before the U.S. Open in June.
The 30-year-old from Abbotsford, B.C., has won on every level of golf from the junior ranks in British Columbia through college and on golf's developmental tours, and says last year's breakthrough on the PGA TOUR was a combination of lots of hard work and a getting some breaks.
Hadwin said his iron play is the key area he's hoping to improve as the 2017-2018 PGA Tour season ramps up. He admitted he wasn't hitting as many greens as he should be last season, but his putting saved him.
"When I'm good, I'm good, and I hit a ton of greens, but it just hasn't been very consistent the last couple years," he said. "That's what we're after now, just dialling in that consistency in certain situations.
Hadwin burst onto the scene in 2011 when he finished tied for fourth at the RBC Canadian Open when it was contested at Shaughnessy Golf and Country Club, just over an hour from where he grew up. He explained he never wanted that result to be the defining moment in his career. With a victory now under his belt, he's looking for more in 2018 and beyond.
"I'm at the point where I don't want the win to be the defining moment of my career either? I don't want it to be 'blah' after that. I'm always striving for more, always trying to get better, and always looking for win No. 2, then win No. 3 and so on and so on for the rest of my career," he said.
Last year was a big year off the course as well for Hadwin, who got married, bought his first home, and bought dog – a cava-poo-chon cross breed named Brad.


text:
Lesbian students enter to cheers at Minn. school
Two lesbian high school students who fought for the right to walk together as part of a royalty court made their entrances Monday to the cheers of hundreds of classmates.
CHAMPLIN, Minn. – Two lesbian high school students who fought for the right to walk together as part of a royalty court made their entrances Monday to the cheers of hundreds of classmates.
Sarah Lindstrom and Desiree Shelton wore matching black suits with pink ties and held hands as they entered the Snow Days Pep Fest at Champlin Park High School in Minneapolis' northwest suburbs.
Students voted onto the royalty court traditionally enter the assembly in boy-girl pairs. After Lindstrom and Shelton, both 18, were elected, school officials last week announced a change in procedure: court members would walk in individually or accompanied by a parent or favorite teacher.
School officials said they merely wanted to prevent the two from being teased. But on Friday, two groups — the Southern Poverty Law Center and the National Center for Lesbian Rights — sued on their behalf.
On Saturday, in federally mediated talks, school officials relented.
Champlin Park is part of the Anoka-Hennepin school district, Minnesota's largest, which has been in the spotlight in the past year for its handling of issues involving gay and lesbian students. Six students have committed suicide in the district since the beginning of the 2009-10 school year, and advocacy groups have linked some of the deaths to the bullying of gay students.
The district said last month its own investigation did not find evidence that bullying contributed to the students' deaths.
Toll of North Carolina Murder-Suicide Rises to 4 as Son Dies
Alaska soldier held on suspicion of espionage
In Pro-Trump Tennessee, Democrats Count on a Familiar Face to Flip a Senate Seat
Overdose Leaves Minnesota Teen Dead, 10 Hospitalized
Molestation investigation shuts California school
GOP adds constitutional amendment to budget fight


Wouldn't you love to enjoy your north Georgia home, cabin porch or patio most of the year, without having to cover furniture during storms, without fighting insects, and without cleaning spring pollen? With Eze-Breeze porch enclosures, YOU CAN!
Eze-Breeze enclosures are a combination of an aluminum frame and sliding panels fitted with a flexible vinyl. This unique design allows you to open the panels as much as you want to enjoy your room as a screened porch or close them to enjoy the room as a 3-season space. Add a fireplace or heater and you can enjoy your outdoor space all year! An Eze-Breeze enclosure offers many of benefits of glass without the expense or maintenance.
Outdoor Living, Indoor Comfort makes it possible for you to quickly, easily and affordably enclose your porch or patio. They service homes, vacation properties and cabins from Blairsville to Atlanta. No matter the size or complexity of your porch or patio enclosure project, Outdoor Living, Indoor Comfort can customize an Eze-Breeze sliding system to turn your space into a timeless oasis.
Whether your patio demands the elegance of a bold and striking structure, your garden needs a charming centerpiece, or you're simply looking to define a space to enjoy the ambiance of a cool evening– a pergola may be the answer you've been searching for. Pergolas do much more than offer shade and design appeal– they create a magical place to bring families and friends together, and Outdoor Living will design and install the pergola of your dreams.
Outdoor Living, Indoor Comfort's aluminum pergolas are essentially maintenance-free and can provide up to a 60% shade coefficient. With no worries of termites, rotting, twisting, cracking or painting, you can relax and just enjoy. Their pergolas offer the thickest gauge aluminum in the industry, which allows for a deep embossing that captures the true texture and the elegant look of premium wood. Whether you are looking for added style on your deck or some tranquil shade, Outdoor Living, Indoor Comfort can turn any outdoor space into the perfect retreat.
One of the hottest new products now offered by Outdoor Living, Indoor Comfort are Cable Railings. These are perfect for north Georgia decks with exceptional views of our mountains and lakes. Outdoor Living, Indoor Comfort can easily bring a sleek and modern look to your deck, balcony, porch, stairs or fence with a premier stainless steel cable railing system that provides you with a safe, strong, low-maintenance guardrail that you can look right through. Cable railings practically disappear, so you can get the most out of your amazing mountain, lake or other scenic view. Outdoor Living, Indoor Comfort also offers maintenance-free aluminum railing systems.
When you are ready to sit back and enjoy your outdoor space, Outdoor Living, Indoor Comfort has everything you need to complete your retreat. Their wide selection of outdoor furniture offers stylish options in a variety of materials designed to withstand the elements. Choose from their woven, composite, wrought iron, cast aluminum or aluminum porch and patio collections, all of which will enhance your investment in comfort.
You may also choose to add custom waterproof flooring and/or durable and fashionable rugs. Complete your look with lamps and other accessories– many styles are available and on display in their showroom. Outdoor Living's products lend style and comfort to fit your lifestyle.
Spring is the perfect time to transform your outdoor space into the summer oasis you've always dreamed of. Let Outdoor Living, Indoor Comfort take you there.


This innovative spring, developed and introduced by Sloting Plus in the world of the Slot, is called "variable pitch" or "variable flexibility". It has been created in the image and likeness of those used in real life and as the name suggests, the turns of spring do not have the same step.
The fixed pitch springs, which are currently used, provide a constant and linear compression behavior and, on the contrary, the variable pitch springs have an adaptive behavior.
At the initial part of the travel the softest area acts and as it is compressed, the middle part acts and becomes more rigid.
Spring for suspension -V1- L10/3-SM20 LONG - SOFT to MEDIUM.
Also we have created a new alphanumeric system to codify the suspension springs. In this way we can correctly classify all the springs.
The first letter of the code, followed by a number, indicates the total length of the spring. After the bar, followed by a number, or numbers (in the case of the conical springs), indicated the inner diameter.
The second and third letter of the code indicates the hardness of the spring and the final number indicates the thickness of the steel wire.
You can see the scheme of the coding in the informative document.
All Sloting Plus springs are compatible with the usual suspension systems on the market for 1/28, 1/32 and 1/24 scale.


text:
import copy
import itertools
import math
import os
import warnings

import _test_helpers
import numpy as np
import pytest
import torch
from _orttraining_ortmodule_models import (
    MyCustomClassInputNet,
    MyCustomFunctionReluModel,
    NeuralNetCustomClassOutput,
    NeuralNetSinglePositionalArgument,
)

from onnxruntime.training.ortmodule import ORTMODULE_TORCH_CPP_DIR, ORTModule, _fallback
from onnxruntime.training.ortmodule.torch_cpp_extensions import is_installed as is_torch_cpp_extensions_installed

# PyTorch model definitions for tests

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_forward(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_FORCE_TORCH_FORWARD policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_FORCE_TORCH_FORWARD"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    from dataclasses import dataclass

    @dataclass
    class Point:
        x: int
        y: int

    class UnsupportedInputModel(torch.nn.Module):
        def __init__(self):
            super(UnsupportedInputModel, self).__init__()

        def forward(self, point):
            return point.x * point.y

    pt_model = UnsupportedInputModel()
    ort_model = ORTModule(copy.deepcopy(pt_model))
    inputs = Point(x=2, y=3)

    ort_model.train(is_training)
    pt_model.train(is_training)

    for i in range(3):
        if fallback_enabled:
            if matching_policy:
                if i > 0 and persist_fallback:
                    assert (
                        ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
                        is not None
                    )
                ort_out = ort_model(inputs)
                pt_out = pt_model(inputs)
                assert ort_out == pt_out
            else:
                with pytest.raises(_fallback.ORTModuleFallbackException) as type_error:
                    ort_model(inputs)
                assert "ORTModule does not support the following model data type" in str(type_error.value)
        else:
            with pytest.raises(_fallback.ORTModuleFallbackException) as type_error:
                ort_model(inputs)
            assert "ORTModule does not support the following model data type" in str(type_error.value)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_device__multiple(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_DEVICE policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DATA) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
        else:
            policy = "FALLBACK_UNSUPPORTED_DATA"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    class ManyDevicesNet(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.net1 = torch.nn.Linear(10, 10).to("cuda:0")
            self.relu = torch.nn.ReLU()
            self.net2 = torch.nn.Linear(10, 5).to("cpu")

        def forward(self, x):
            x = self.relu(self.net1(x.to("cuda:0")))
            return self.net2(x.to("cpu"))

    pt_model = ManyDevicesNet()
    inputs = torch.randn(20, 10)

    for _ in range(3):
        if fallback_enabled:
            if matching_policy:
                ort_model = ORTModule(copy.deepcopy(pt_model))
                pt_model.train(is_training)
                ort_model.train(is_training)
                ort_out = ort_model(inputs)
                pt_out = pt_model(inputs)
                _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=0, atol=0)
            else:
                with pytest.raises(_fallback.ORTModuleFallbackException) as type_error:
                    # Initialize with fallback policy because Exception will happen during __init__
                    ort_model = ORTModule(copy.deepcopy(pt_model))
                assert "ORTModule supports a single device per model" in str(type_error.value)
        else:
            with pytest.raises(_fallback.ORTModuleFallbackException) as type_error:
                # Initialize with fallback policy because Exception will happen during __init__
                ort_model = ORTModule(copy.deepcopy(pt_model))
            assert "ORTModule supports a single device per model" in str(type_error.value)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_device__mismatch(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_DEVICE policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DATA) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
        else:
            policy = "FALLBACK_UNSUPPORTED_DATA"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)
    os.environ["ORTMODULE_SKIPCHECK_POLICY"] = "SKIP_CHECK_DISABLED"

    data_device = "cuda"
    N, D_in, H, D_out = 64, 784, 500, 10

    pt_model = NeuralNetSinglePositionalArgument(D_in, H, D_out)
    ort_model = ORTModule(copy.deepcopy(pt_model))
    pt_model.train(is_training)
    ort_model.train(is_training)
    # For initial model export, use same device for data and model so that PyTorch model can be traced during export
    _ = ort_model(torch.randn(N, D_in))

    # Use data in different device for testing
    inputs = torch.randn(N, D_in, device=data_device)
    ort_model_device = ort_model._torch_module._execution_manager(ort_model._is_training())._device
    input_device = torch.device(inputs.device)

    for _ in range(3):
        if fallback_enabled:
            if matching_policy:
                with pytest.raises(RuntimeError) as e:
                    ort_model(inputs)
                assert (
                    "Tensor for argument #1 'self' is on CPU, but expected them to be on GPU (while checking arguments for addmm)"
                    in str(e.value)
                ) or (
                    "Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0!"
                    in str(e.value)
                )
            else:
                with pytest.raises(_fallback.ORTModuleDeviceException) as e:
                    ort_model(inputs)
                assert (
                    f"Input argument to forward found on device {input_device}, "
                    f"but expected it to be on module device {ort_model_device}." in str(e.value)
                )
        else:
            with pytest.raises(_fallback.ORTModuleDeviceException) as e:
                ort_model(inputs)
            assert (
                f"Input argument to forward found on device {input_device}, "
                f"but expected it to be on module device {ort_model_device}." in str(e.value)
            )

    del os.environ["ORTMODULE_SKIPCHECK_POLICY"]

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_output(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_DATA policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_DATA"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    device = "cuda"
    N, D_in, H, D_out = 64, 784, 500, 10
    pt_model = NeuralNetCustomClassOutput(D_in, H, D_out).to(device)
    ort_model = ORTModule(copy.deepcopy(pt_model))
    x = torch.randn(N, D_in, device=device)
    y = torch.randn(N, D_in, device=device)
    z = torch.randn(N, D_in, device=device)

    ort_model.train(is_training)
    pt_model.train(is_training)

    for i in range(3):
        if fallback_enabled:
            if matching_policy:
                if i > 0 and persist_fallback:
                    assert (
                        ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
                        is not None
                    )
                ort_out = ort_model(x, y, z)
                pt_out = pt_model(x, y, z)
                _test_helpers.assert_values_are_close(ort_out.out1, pt_out.out1, rtol=0, atol=0)
                _test_helpers.assert_values_are_close(ort_out.out2, pt_out.out2, rtol=0, atol=0)
                _test_helpers.assert_values_are_close(ort_out.out3, pt_out.out3, rtol=0, atol=0)
            else:
                with pytest.raises(_fallback.ORTModuleIOError) as runtime_error:
                    ort_model(x, y, z)
                assert "ORTModule does not support the following model output type" in str(runtime_error.value)
        else:
            with pytest.raises(_fallback.ORTModuleIOError) as runtime_error:
                ort_model(x, y, z)
            assert "ORTModule does not support the following model output type" in str(runtime_error.value)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_input(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_DATA policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_DATA"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    pt_model = MyCustomClassInputNet()
    ort_model = ORTModule(copy.deepcopy(pt_model))
    inputs = torch.randn(1, 2)

    class CustomClass:
        def __init__(self, x):
            self.x = x

    ort_model.train(is_training)
    pt_model.train(is_training)

    for i in range(3):
        if fallback_enabled:
            if matching_policy:
                if i > 0 and persist_fallback:
                    assert (
                        ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
                        is not None
                    )
                ort_out = ort_model(inputs, CustomClass(1))
                pt_out = pt_model(inputs, CustomClass(1))
                _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=0, atol=0)
            else:
                with pytest.raises(_fallback.ORTModuleIOError) as ex_info:
                    _ = ort_model(torch.randn(1, 2), CustomClass(1))
                assert (
                    "ORTModule does not support the following model data"
                    " type <class 'orttraining_test_ortmodule_fallback."
                    "test_ortmodule_fallback_input.<locals>.CustomClass'>" in str(ex_info.value)
                )
        else:
            with pytest.raises(_fallback.ORTModuleIOError) as ex_info:
                _ = ort_model(torch.randn(1, 2), CustomClass(1))
            assert (
                "ORTModule does not support the following model data"
                " type <class 'orttraining_test_ortmodule_fallback."
                "test_ortmodule_fallback_input.<locals>.CustomClass'>" in str(ex_info.value)
            )

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_torch_model(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_TORCH_MODEL policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_TORCH_MODEL"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    device = "cuda"
    N, D_in, H, D_out = 64, 784, 500, 10
    x = torch.randn(N, D_in, device=device)

    pt_model = NeuralNetSinglePositionalArgument(D_in, H, D_out).to(device)
    pt_model = torch.nn.DataParallel(pt_model)

    for _ in range(3):
        if fallback_enabled:
            if matching_policy:
                ort_model = ORTModule(pt_model)
                ort_model.train(is_training)
                pt_model.train(is_training)

                ort_out = ort_model(x)
                pt_out = pt_model(x)
                _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=1e-3, atol=1e-6)
            else:
                with pytest.raises(_fallback.ORTModuleTorchModelException) as ex_info:
                    ort_model = ORTModule(pt_model)
                assert "ORTModule is not compatible with torch.nn.DataParallel" in str(ex_info.value)
        else:
            with pytest.raises(_fallback.ORTModuleTorchModelException) as ex_info:
                # Initialize with fallback policy because Exception will happen during __init__
                ort_model = ORTModule(pt_model)
            assert "ORTModule is not compatible with torch.nn.DataParallel" in str(ex_info.value)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_init__torch_version(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_TORCH_MODEL policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    from packaging import version

    from onnxruntime.training.ortmodule import MINIMUM_RUNTIME_PYTORCH_VERSION_STR

    runtime_pytorch_version = version.parse(torch.__version__.split("+")[0])
    minimum_runtime_pytorch_version = version.parse(MINIMUM_RUNTIME_PYTORCH_VERSION_STR)
    if runtime_pytorch_version < minimum_runtime_pytorch_version:

        if fallback_enabled:
            if matching_policy:
                policy = "FALLBACK_BAD_INITIALIZATION"
            else:
                policy = "FALLBACK_UNSUPPORTED_DEVICE"
        else:
            policy = "FALLBACK_DISABLE"
        os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
        os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

        device = "cuda"
        N, D_in, H, D_out = 64, 784, 500, 10
        x = torch.randn(N, D_in, device=device)

        pt_model = NeuralNetSinglePositionalArgument(D_in, H, D_out).to(device)

        for i in range(3):
            if fallback_enabled:
                if matching_policy:
                    ort_model = ORTModule(pt_model)
                    ort_model.train(is_training)
                    pt_model.train(is_training)

                    ort_out = ort_model(x)
                    pt_out = pt_model(x)
                    _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=0, atol=0)
                else:
                    with pytest.raises(_fallback.ORTModuleInitException) as ex_info:
                        ort_model = ORTModule(pt_model)
                    assert "ONNX Runtime ORTModule frontend requires PyTorch version greater or equal to" in str(
                        ex_info.value
                    )
            else:
                with pytest.raises(_fallback.ORTModuleInitException) as ex_info:
                    # Initialize with fallback policy because Exception will happen during __init__
                    ort_model = ORTModule(pt_model)
                assert "ONNX Runtime ORTModule frontend requires PyTorch version greater or equal to" in str(
                    ex_info.value
                )
    else:
        warnings.warn(
            "Skipping test_ortmodule_fallback_torch_version."
            f" It requires PyTorch prior to {MINIMUM_RUNTIME_PYTORCH_VERSION_STR}"
        )

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_init__missing_cpp_extensions(
    is_training, fallback_enabled, matching_policy, persist_fallback
):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_TORCH_MODEL policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if is_torch_cpp_extensions_installed(ORTMODULE_TORCH_CPP_DIR):
        warnings.warn(
            "Skipping test_ortmodule_fallback_init__missing_cpp_extensions."
            f" It requires PyTorch CPP extensions to be missing"
        )
    else:

        if fallback_enabled:
            if matching_policy:
                policy = "FALLBACK_BAD_INITIALIZATION"
            else:
                policy = "FALLBACK_UNSUPPORTED_DEVICE"
        else:
            policy = "FALLBACK_DISABLE"
        os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
        os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

        device = "cuda"
        N, D_in, H, D_out = 64, 784, 500, 10
        x = torch.randn(N, D_in, device=device)

        pt_model = NeuralNetSinglePositionalArgument(D_in, H, D_out).to(device)

        for _ in range(3):
            if fallback_enabled:
                if matching_policy:
                    ort_model = ORTModule(pt_model)
                    ort_model.train(is_training)
                    pt_model.train(is_training)

                    ort_out = ort_model(x)
                    pt_out = pt_model(x)
                    _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=0, atol=0)
                else:
                    with pytest.raises(_fallback.ORTModuleInitException) as ex_info:
                        ort_model = ORTModule(pt_model)
                    assert "ORTModule's extensions were not detected" in str(ex_info.value)
            else:
                with pytest.raises(_fallback.ORTModuleInitException) as ex_info:
                    # Initialize with fallback policy because Exception will happen during __init__
                    ort_model = ORTModule(pt_model)
                assert "ORTModule's extensions were not detected" in str(ex_info.value)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_onnx_model__custom_autograd(
    is_training, fallback_enabled, matching_policy, persist_fallback
):
    from onnxruntime.training.ortmodule._custom_autograd_function import (
        custom_autograd_function_enabler,
        enable_custom_autograd_support,
    )

    # Disable the autograd support to test the fallback.
    old_state = custom_autograd_function_enabler.state
    enable_custom_autograd_support(False)

    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_ONNX_MODEL policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_ONNX_MODEL"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    dtype = torch.float
    device = torch.device("cuda")
    N, D_in, H, D_out = 64, 1000, 100, 10

    x = torch.randn(N, D_in, device=device, dtype=dtype)
    y = torch.randn(N, D_out, device=device, dtype=dtype)
    w1 = torch.randn(D_in, H, device=device, dtype=dtype, requires_grad=True)
    w2 = torch.randn(H, D_out, device=device, dtype=dtype, requires_grad=True)

    pt_model = MyCustomFunctionReluModel()
    ort_model = ORTModule(copy.deepcopy(pt_model))
    ort_model.train(is_training)
    pt_model.train(is_training)

    for i in range(3):
        if fallback_enabled:
            if matching_policy:
                if i > 0 and persist_fallback:
                    assert (
                        ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
                        is not None
                    )
                pt_out = pt_model(x.mm(w1)).mm(w2)
                ort_out = ort_model(x.mm(w1)).mm(w2)
                _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=1e-03, atol=1e-04)
            else:
                with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
                    _ = ort_model(x.mm(w1)).mm(w2)
                assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)
        else:
            with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
                # Initialize with fallback policy because Exception will happen during __init__
                _ = ort_model(x.mm(w1)).mm(w2)
            assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)

    # Restore the autograd support state.
    enable_custom_autograd_support(old_state)

@pytest.mark.parametrize(
    "is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
)
def test_ortmodule_fallback_onnx_model__missing_op(is_training, fallback_enabled, matching_policy, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
    # matching_policy: True matches FALLBACK_UNSUPPORTED_ONNX_MODEL policy to ORTModuleDeviceException exception.
    #   Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen

    if fallback_enabled:
        if matching_policy:
            policy = "FALLBACK_UNSUPPORTED_ONNX_MODEL"
        else:
            policy = "FALLBACK_UNSUPPORTED_DEVICE"
    else:
        policy = "FALLBACK_DISABLE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)

    class CrossModule(torch.nn.Module):
        def forward(self, x, y):
            return torch.cross(x, y)

    x = torch.randn(2, 3)
    y = torch.randn(2, 3)

    pt_model = CrossModule()
    ort_model = ORTModule(copy.deepcopy(pt_model))
    ort_model.train(is_training)
    pt_model.train(is_training)

    for i in range(3):
        if fallback_enabled:
            if matching_policy:
                if i > 0 and persist_fallback:
                    assert (
                        ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
                        is not None
                    )
                pt_out = pt_model(x, y)
                ort_out = ort_model(x, y)
                _test_helpers.assert_values_are_close(ort_out, pt_out, rtol=0, atol=0)
            else:
                with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
                    _ = ort_model(x, y)
                assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)
        else:
            with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
                # Initialize with fallback policy because Exception will happen during __init__
                _ = ort_model(x, y)
            assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)

@pytest.mark.parametrize("is_training,persist_fallback", list(itertools.product([True, False], repeat=2)))
def test_ortmodule_fallback_warn_message(is_training, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise

    policy = "FALLBACK_UNSUPPORTED_DEVICE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)
    os.environ["ORTMODULE_SKIPCHECK_POLICY"] = "SKIP_CHECK_DISABLED"

    data_device = "cuda"
    N, D_in, H, D_out = 64, 784, 500, 10

    pt_model = NeuralNetSinglePositionalArgument(D_in, H, D_out)
    ort_model = ORTModule(copy.deepcopy(pt_model))
    pt_model.train(is_training)
    ort_model.train(is_training)
    # For initial model export, use same device for data and model so that PyTorch model can be traced during export
    _ = ort_model(torch.randn(N, D_in))

    # Use data in different device for testing
    inputs = torch.randn(N, D_in, device=data_device)

    for _ in range(3):
        with pytest.raises(RuntimeError):
            with pytest.warns(UserWarning) as warning_record:
                ort_model(inputs)
        assert "Fallback to PyTorch due to exception" in str(warning_record[0].message.args[0])

    del os.environ["ORTMODULE_SKIPCHECK_POLICY"]

@pytest.mark.parametrize("is_training,persist_fallback", list(itertools.product([True, False], repeat=2)))
def test_ortmodule_fallback_non_contiguous_tensors(is_training, persist_fallback):
    # is_training: True for torch.nn.Module training model, eval mode otherwise
    # Validate fix for issue: https://github.com/pytorch/ort/issues/92

    policy = "FALLBACK_UNSUPPORTED_DEVICE"
    os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
    os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)
    os.environ["ORTMODULE_SKIPCHECK_POLICY"] = "SKIP_CHECK_DISABLED"

    class PositionalEncoding(torch.nn.Module):
        def __init__(self, d_model, dropout=0.1, max_len=5000):
            super().__init__()
            self.dropout = torch.nn.Dropout(p=dropout)
            position = torch.arange(max_len).unsqueeze(1)
            div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
            pe = torch.zeros(max_len, 1, d_model)
            pe[:, 0, 0::2] = torch.sin(position * div_term)
            pe[:, 0, 1::2] = torch.cos(position * div_term)
            self.register_buffer("pe", pe)

        def forward(self, x):
            x = x + self.pe[: x.size(0)]
            return self.dropout(x)

    class TransformerModel(torch.nn.Module):
        def __init__(self, ntoken, d_model, nhead, d_hid, nlayers, dropout=0.5):
            super().__init__()
            self.model_type = "Transformer"
            encoder_layers = torch.nn.TransformerEncoderLayer(d_model, nhead, d_hid, dropout)
            self.transformer_encoder = torch.nn.TransformerEncoder(encoder_layers, nlayers)
            self.pos_encoder = PositionalEncoding(d_model, dropout)
            self.encoder = torch.nn.Embedding(ntoken, d_model)
            self.d_model = d_model
            self.decoder = torch.nn.Linear(d_model, ntoken)
            self.init_weights()

        def init_weights(self):
            initrange = 0.1
            self.encoder.weight.data.uniform_(-initrange, initrange)
            self.decoder.bias.data.zero_()
            self.decoder.weight.data.uniform_(-initrange, initrange)

        def forward(self, src, src_mask):
            src = self.encoder(src) * math.sqrt(self.d_model)
            src = self.pos_encoder(src)
            output = self.transformer_encoder(src, src_mask)
            output = self.decoder(output)
            return output

    def generate_square_subsequent_mask(sz):
        return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1)

    def get_batch(source, i):
        seq_len = min(bptt, len(source) - 1 - i)
        data = source[i : i + seq_len]
        target = source[i + 1 : i + 1 + seq_len].reshape(-1)
        return data, target

    criterion = torch.nn.CrossEntropyLoss()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    train_data = np.random.randint(1, 12455, 1000)
    ends = np.random.randint(2, 20, 100).cumsum()
    ends = ends[ends < train_data.shape[0] - 2]
    train_data[ends] = 0
    train_data[-1] = 0

    train_data = torch.tensor(np.array(train_data, dtype=np.int64))
    train_data = train_data.to(torch.int64).to(device)
    bptt = 35
    src_mask = generate_square_subsequent_mask(bptt).to(device)
    ntokens, emsize, nhead, d_hid, nlayers, dropout = 12455, 200, 2, 200, 2, 0.2
    pt_model = TransformerModel(ntokens, emsize, nhead, d_hid, nlayers, dropout)
    model = ORTModule(pt_model).to(device)
    pt_model.train(is_training)
    model.train(is_training)
    optimizer = torch.optim.SGD(model.parameters(), lr=5.0)

    n_iter = 0
    for epoch in range(1, 2):
        model.train()  # turn on train mode

        num_batches = len(train_data) // bptt
        for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
            data, targets = get_batch(train_data, i)
            batch_size = data.size(0)
            if batch_size != bptt:  # only on last batch
                src_mask = src_mask[:batch_size, :batch_size]
            output = model(data, src_mask)
            nrows = min(ntokens, targets.shape[0])
            loss = criterion(output.view(nrows, -1), targets)

            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
            optimizer.step()
            n_iter += 1
            break

    assert n_iter > 0

    del os.environ["ORTMODULE_SKIPCHECK_POLICY"]

redpajama_set_name: RedPajamaGithub


Last night news broke that BT is set to be a likely takeover target for Deutsche Telekom according to senior industry executives and city analysts.
Speculation of a potential deal emerged as the German outfit is in advanced talks to merge its American arm, T-Mobile US, with the satellite pay-TV operator Dish Networks. If a deal is completed the likelihood of a BT takeover will increase.
Having previously struggled in the US market the T-Mobile Dish merger could be seen as a route to exit the American market. Concentrating their efforts in Europe with BT would go a long way to Deutsche Telekom realising their ambitions to join up their networks across international borders.
BT's equity is currently valued at £37bn, having gained 40pc in the last two years as the company has benefitted from its broadband network upgrade and attacked Sky's pay-TV dominance.
The Germans are already due to become major shareholders in BT following the completion of its £12.5bn takeover of EE, expected early next year.
Under the terms of the EE deal, Deutsche Telekoms stake in BT cannot exceed 15pc for three years following completion, meaning it cannot initiate a deal until 2019.


Posted in Book News on June 23rd, 2015 | Comments Off on Come in and Read!
Posted in Book News on June 7th, 2015 | Comments Off on Study Now!
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Building a math or science lesson plan? Check out the recently purchased AIMS Activities Guides Some of the more than 50 titles included in the library collections are: These and other lesson planning materials are available for your use in the Curriculum Resource Center.
What are you going to do on Spring Break? How about reading for fun? Not available through the Curriculum Resource Center collections? Request it through OHIOLink!


Methanol is a type of alcohol that is used as organic solvent . Methanol is also known by other names such as methyl alcohol , " wood alcohol" or "spirit" . At room temperature , methanol is a liquid form of light, volatile, colorless, flammable and has a unique smell that is slightly sweet and light than ethanol (drinking alcohol). In addition to the organic solvent, in the industrial, methanol is also used for anti- freeze coolant, windscreen detergent, liquid carburetor, paint thinner, varnish, fuel and as a denaturant for ethanol industry.
Methanol poisoning can be caused by intentional or unintentional . Poisoning can be caused by drinking methanol -containing materials with the intention to commit suicide, exposure to methanol through the work environment (through absorption in the skin or respiratory tract) or abuse of methanol as a bootlegger.
Methanol is used as a substitute for ethanol because the price is relatively cheaper and also the consequences of not understanding the dangers that can be caused by both these materials, so that many of them think that the nature and function are the same methanol . This results in people who are already addicted to liquor and unable to legally purchase liquor tends to make or buy illegal liquor which is mixed with methanol.
In the body, methanol is easily absorbed into the body either through the skin, respiratory tract or oral. Methanol itself is not dangerous, but the harmful metabolites can lead to major problems such as accumulation of acid in the blood (metabolic acidosis), permanent blindness and death.
A person with methanol poisoning may not show any symptoms for a period of 1-72 hours. The symptoms will form more quickly if methanol is consumed in large quantities and is slower if it is taken in small quantities or it taken with ethanol . Complications such as blindness may exist even with only drinking 5-10ml of methanol and potentially fatal if taken in excess of 30ml and almost certainly fatal if taken in excess of 100ml.
In the case of methanol poisoning, likely to be difficult to get diagnosis through the history from the patients, but it may be suspected if the patient has symptoms such as loss of consciousness, vision problems or problems of accumulation of acid in the blood (metabolic acidosis) or when a patient with high-risk occupations are exposed to methanol.
Early symptoms of patients have problems of coordination and balance, a low level of awareness and confusion. In addition, patients may have symptoms that are not specific as body weakness, dizziness, headache, nausea, vomiting and abdominal pain. In serious cases, patients can come with seizures and coma.
Approximately 50 % of patients will have vision problems as well as with the symptoms above. With regard to the problem of vision, patients will have blurred vision or saw yellow spots in vision or eye pain when they have seen the light. Patients may also come with blindness.
In addition to the above symptoms, patients with methanol poisoning can have complications such as respiratory failure, heart failure and kidney failure.
Prognosis for methanol poisoning is closely related to the severity of the problem of the accumulation of acid in the blood (metabolic acidosis), the time to come to the hospital for treatment and the length of time for the treatment to start, which is within 8 hours of methanol ingestion.
The prognosis is worse if the patient comes in a coma state or convulsions.
Patients who survive may have permanent blindness and nerve problems such as difficulty in walking and moving (parkinsonism), peripheral nerve problems, problems in thinking, difficulty in concentrating, memory problems and seizures.
In any case of poisoning, the most important thing is to ensure the respiratory and cardiovascular function are intact and perfect.
If the patient is exposed to methanol, either through eye, skin or oral, make sure the patient was transferred to a safe place and away from the source of the poisoning.
If contact to the eyes, the eyes should be washed continuously with tap water for at least 15 minutes and get immediate medical attention.
If the patient has difficulty in breathing, give oxygen supplement and to give ventilatory support if the situation warrants and immediately seek medical attention .
If the patient accidentally ingested it, do not force the patient to vomit and seek medical attention immediately.
The main goal in the treatment of methanol poisoning is to prevent methanol to be converted by the body to harmful substances and also to remove the harmful substances from the body.
For that purpose, the antidote should be given to the patient, that is ethanol or fomepizole. It should be given as soon as the patient arrives at the hospital for treatment.
Hemodialysis treatment used to speed up the removal of the harmful substances from the body.
Using full personal protective equipment when working in an environment that contains methanol.
Eliminate production , smuggling and sale of illegal and contaminated liquor through law enforcement.
Continuous and effective education to consumers regarding the dangers of illegal and contaminated.


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Utah State University - » Elements chef named valley's best at Spice on Ice
Elements chef named valley's best at Spice on Ice
April 5th, 2010 Posted in Arts and Life
By Kate Clark
NORTH LOGAN–Cache Valley residents dined in winter coats at the chilly George S. Eccles Ice Center for the seventh annual Spice on Ice charity event recently, raising over $30,000 for the non-profit arena.
"We only expected to earn around $22,000," said Spice on Ice Chairman Tommy Kite. "This was the most successful event we've had in the past few years. It was a pleasant surprise that it turned out so well."
The money raised every year through Spice on Ice is a major source of funding the non-profit ice center relies on to keep the building open to the community.
The event featured chefs from Tandoori Oven, Elements, Indian Oven, Smokin' Blues Barbeque, Utah State Catering, Firehouse Pizza and Hamilton's Steak and Seafood, all locally owned and operated restaurants in the valley. Each chef prepared a signature entrée that was served to participants, who purchased $50 tickets to attend and vote for the dish of their choice. The entrées were also served to four judges–two chefs, a member of the USU dietetics program and the 2008 Spice on Ice Chef of the Year, Scott Norbridge.
After plates were licked clean and participants were moved to a warmer area of the building, executive chef Dustin McKay, from Elements, was announced as winner of this year's Spice on Ice Chef of the Year for his blue cheese rib-eye steak with mashed potatoes.
"There are so many tastes, so we really had to think about what would appeal to the most people," said McKay. "We (Elements) pride ourselves on our food, and we hoped that other people would too."
Although McKay won the major award for the year, Kite commended all of the restaurants that participated in the cook-off.
"All of the chefs put in their best. It's a big donation on their end. They donate a lot of time and money on the food they prepare for this event."
Indian Oven chef and owner Ash Oberoi said, "We're not discouraged. It's not about winning the contest. This is a great source of fund-raising; it's about getting together and meeting new people."
Fee door-dent removals, Utah Jazz tickets and a trip to the Kennedy Space Center were among many items auctioned following the dinner and awards, earning a major portion of the evening's donations.
The event also featured appetizers from Culinary Concepts, Pounders, Pioneer Valley Lodge, Café Sabor and the Logan Golf and Country Club. Chocolate torte prepared by Dewayne Phillips of USU Dining Services and Café Ibis coffee were served for dessert.
"To raise money is always a good thing," said Eliza Couch, an attendee. "And I think it's good for people to try different foods and broaden their horizons."
Everyone seemed pleased with the evening, especially those who spent countless hours preparing for the event.
"We put in over 900 hours prior to the event to pull off this dinner," executive director Floyd Naegle said. "We had seven of the top restaurants and chefs. I don't think there is a place in the state where you can go and sample food like this. We really appreciate the community support."
Kite agrees. "We're heavily invested in the ice center and seeing it succeed. It makes us feel good that we're doing something for the community and giving people a place to come and enjoy themselves."
(Correction appended April 12: The money raised every year through Spice on Ice is a major source of funding for the non-profit ice center, not the only source.)
Tags: chef of the year, fundraiser, Spice on Ice


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Federal Judge Strikes Down Georgia's Compassionate Heartbeat Law
July 13, 2020 CONTACT: Prudence Robertson
probertson@sbalist.org
Washington, D.C. – Today an Atlanta federal judge struck down Georgia's pro-life law which protects unborn children from abortion when a heartbeat can be detected. The law was signed by Governor Brian Kemp in May of last year.
"The beating heart of a vulnerable unborn child should awaken the conscience of our nation, and Georgia helped lead the way with the passage of its heartbeat bill," said SBA List President Marjorie Dannenfelser. "We are disappointed that the will of the people is being thwarted with today's decision striking down this compassionate law. We thank Governor Brian Kemp for championing this legislation, and we stand with pro-life Georgians in the fight to protect this strong, compassionate pro-life law."
Last year, Governor Brian Kemp stood up to Hollywood and the film industry when more than 40 celebrities threatened to boycott the state on account of its strong protections for unborn children.
Dr. Kathi Aultman, a former abortionist and an associate scholar with SBA List's research arm Charlotte Lozier Institute, traveled to Georgia to give testimony on the bill before it became law.
SBA List is a network of more than 837,000 pro-life Americans nationwide, dedicated to ending abortion by electing national leaders and advocating for laws that save lives, with a special calling to promote pro-life women leaders.
Posted in Home, Press Releases


Rogue Pacific carries a diverse stock of reclaimed wood mantel materials to fit just the look you are going for. From Hand Hewn Oak, Hickory and Poplar, to Black Walnut, Heart Pine, Doug Fir and Chestnut, there are a wide variety of materials to fit the custom look you are trying to achieve. Our experienced crew treats this historical wood with care, from the beginning of the process to the end.
There is a nice selection of old reclaimed wood mantels on display in the Medford, Oregon lumber yard.


text:
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Domestic abuse and support in the workplace – what needs to change?
Support in the workplace is critical for women experiencing domestic abuse, and for some it can be a lifeline. This is especially pertinent at the moment when the COVID-19 pandemic means most employees are now working from home. For those living with abusive partners, they might now find themselves trapped with their abuser for significant periods of time and may find it more difficult to reach out for advice and support.
Perspective matters when it comes to securing social justice: the Scottish Feminist Judgments Project
by Sharon Cowan, Chloë Kennedy and Vanessa Munro
What is the Scottish Feminist Judgments Project (SFJP)? The SFJP is a collaborative legal project involving over 40 contributors: university academics, lawyers, judges, activists and artists. The project examines sixteen important legal cases in Scotland, and rewrites the judges' decisions as if the judge had been a feminist, using only the resources that would have been available to the judge at the time of the original case.
Child Welfare Reports: what are they and why do they matter in domestic abuse cases?
Over the past four years, Richard Whitecross, a legal researcher at Edinburgh Napier University, has been doing research on how the courts handle child contact cases in which there has been domestic abuse. He has interviewed lawyers, sheriffs and many women with children.
One of the key concerns raised with him by mothers with experience of domestic abuse is about the Child Welfare Report prepared for the court. With support from the Royal Society of Edinburgh, he is beginning a new project to look at Child Welfare Reports and how they report, or not, domestic abuse.
Shedding light on the challenges faced by BAME women in Scotland
As part of a feminist leadership group based in Scotland, we have published a series of blogs from BAME women in Scotland, highlighting the challenges and inequalities many Black and ethnic minority women are facing.
The blogs are written by members of the Feel Good Women's Group in Govanhill, the Ubuntu Women Shelter, members of the Young Women's Movement in Scotland and Zero Tolerance. They discuss a variety of issues including community activism, the impact of the policy of no recourse to public funds, personal experiences of racism and sexism and the links between anti-racism work and prevention of violence against women.
Me, I have a voice
In this blog we share the powerful account of a survivor who endured years of sexual harassment from her male work colleagues. Her testimony brings to light the many obstacles she faced to report the perpetrators and put an end to the abuse, as well as the emotional impact on her life and the journey to heal from this devastating experience.
Content warning: this story includes descriptions of physical and sexual harassment, suicidal thoughts, depression, and references to domestic abuse and sexual violence. You can find details of support services here and at the end of this blog post.
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One of the great blessings of this ministry is the relationships made with others who share the work around the world. Whether that connection is made through email or in person, we truly feel we are members of One Family in Mission, working constantly to help each other awaken a missionary spirit in the people of our own dioceses and countries.
Few have been better brothers to me than Fr. Bernard Makadani Zulu, National Director of the Pontifical Mission Societies for Zambia. Fr. Bernard is known to regular readers as my host during my mission experience in his country in June 2011. He is also known across the Archdiocese of Boston as a participant in our office's Mission Co Operative program during 2012; he came from Zambia for five weeks and preached mission appeals for the Propagation of the Faith in Whitman, Holliston, Bellingham, Reading and Methuen. He also spoke to Religious Education children in Plymouth for the Missionary Childhood Association! He touched so many here in the Archdiocese and continues that work through emails, letters and pictures that he sends to our office so that we can do our job – informing you, our donors, about the vital work being done because of your prayers and sacrifices.
Fr. Bernard has shared a letter and pictures with me from one of the most vital programs run in a Zambian diocese - that of helping HIV positive mothers and their babies. In a letter from Sr. Rosa Margarita, Fr. Bernard was told that thanks to support from the Propagation of the Faith, the Mother and Infant Care program in Mongu, Zambia has met and even exceeded their goal of reducing the transmission of HIV from mother to baby through breast milk by 80% in the program participants. Their methods are simple – educating HIV positive mothers about the necessity of using baby formula instead of breastfeeding and providing an easy way to clean and store water to make the formula in a sanitary manner. The mothers also receive other nutritional supplements for themselves and their babies and further their own vocational training.
To read more about the vital work being done through your generosity and prayers, visit www.propfaithboston.org.
As most of you know, the Missionary Cooperative Program of The Society for the Propagation of the Faith places a missionary bishop, priest, sister, brother, lay person, or member of our office staff in every parish for one weekend a year. The purpose of "MCP" is twofold: first, to share information about missionary work and second to raise prayerful and financial support for that work.
One of the most rewarding aspects of my assignment as Archdiocesan Director is the opportunity to visit some of our parishes to speak at the weekend Masses about the work of the missions. In addition to bringing this "mission message" to the parishioners, it gives me the opportunity to renew acquaintances with our priests, many of whom I already know.
This year I began my Missionary Co-Op preaching at Holy Rosary Parish in Winthrop. Subsequent weekends were spent at Our Lady of Sorrows in Sharon, St. Susanna in Dedham, St. John the Baptist in Quincy and, most recently, Sacred Heart Parish in Watertown. Still to be visited are St. Margaret Mary in Westwood and Saint Ann Parish in Peabody. Every parish is different, but I find in all of them openness to the message of prayer and support for the missionary work of the Church.
My visit to Sacred Heart Parish in Watertown recently was really like "going home." In forty years as a priest, I have spent most of my time in the Waltham, Belmont and Watertown area, and so I knew many of the parishioners by name; their capable and affable pastor Father Joseph Curran and I were in the seminary together. It was a pleasure to spend time in such familiar and friendly surroundings. As I continue my journey around the Archdiocese, I look forward to visiting more parishes during the coming year to spread the good news about the Society of the Propagation of the Faith and about the heroic witness of the missionaries who carry the Gospel message to every corner of the world.


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Q: Can App Passwords For Two-Factor Authentication Be Used for More than One Device? Hi I recently set up my Microsoft account with two-factor authentication. It made me wonder when I used an app password for Outlook - can the app password be used for more than one device? Say a "hacker" put a trojan on my computer and saw the app password when it was generated... would that app password be limited to use on one device only, in my case only on the Outlook install on my computer and not also for some nefarious use by the degenerate scum? Does it depend on the implementation say Google vs Microsoft vs anybody else?
Thanks!

redpajama_set_name: RedPajamaStackExchange


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We live in an increasingly connected world, where much of our lives is being captured digitally as data; our transactions, our interactions, our movements. And as more things become connected through the Internet-of-Things (IoT), even more data is being generated. This is known as big data.
Even though the data is generated by individuals, it doesn't belong to us but to those collecting it, ie. the supermarket that tracks our purchases, the search engine that monitors our searches. And even if our own data is returned to us, we would not really know how to use it because the data is presented in a way to help the institution that collected it, not us.
As individuals, we are also become increasingly worried about online privacy and security, and some of us are starting to withdraw from being too digitally visible. Governments may start addressing these privacy and security issues through regulation, thereby increasing costs. This could make institutions reluctant to invest in innovation, which in turn could lead to a downward spiral – less innovation, less business opportunities, less jobs.
There is a need to reverse this and create an upward spiralling effect on the digital economy. And this is what the Hub-of-All-Things (HAT) project aims to do.
HAT is funded by Digital Economy / NEMODE project £1.2 million 2 years.


on board, 23 x 32"
oil on board, 23 3/4 x 32".
masonite, 22 2/3 x 35".
board, 16 x 15 3/4".
(George Stern Fine Arts, West Hollywood) The modernist aesthetic that painter Conrad Buff (1886-1975) developed in the almost three quarters of a century that he spent working in Los Angeles and Southern California reveals, yet again, the local experimentations with modernism that are still being assessed and understood. But in looking closely at this work it is obvious that Buff was, if not overtly aware of the tendencies and experimentation with an emerging abstraction, certainly a practitioner of a language which fit well into its parameters. The innovative role that this painter, lithographer, illustrator and muralist played in local history is pivotal to this retrospective, which will include nearly fifty paintings drawn from the artist's estate. Also featured is the publication of a new book on the artist, The Art & Life of Conrad Buff, by Will South.
Born in Speicher, Switzerland, Buff trained as a lace designer at the School of Arts and Crafts in St. Gallen, Switzerland, and later studied in Munich. He arrived in the United States in 1905, settling to Wisconsin where he briefly worked as a sheepherder before coming to Los Angeles in 1906. While working here as a house painter, he began making trips into the deserts of Utah, New Mexico and Arizona. This encounter with the Southwestern landscape was the initial reason for his turning to painting. He also began a life-long friendship with painter Maynard Dixon, who often joined him on his trips to the Southwestern desert.
Buff's passion for landscape painting was concomitant with the times: landscape painting was the genre cultivated by many artists in Southern California during the first four decades of the last century, and it produced a number of masters. Yet Buff's approach to nature and its awesome grandeur differed in specific painterly directions from the plein aire style which dominated the local scene. Instead of the interplay between light and color, a bright, bold palette appears. Instead of referencing to Postimpressionism, a tendency toward abstraction emerges.
Buff's palette has the boldness of the fauves but it is decidedly Southwestern, influenced without a doubt by the strong colors of the landscape itself and more subtly, by the turquoises, sienna browns and yellows associated with a Native American aesthetic.
There is a strong tendency toward an architectural spatial vision. Cliffs resemble skyscrapers and their layers of strata become foundations. Geometric in his compositions, he even explored a spatial dichotomy, often dividing the canvas in two, creating two views, two corresponding segments of the same vision.
Two bi-lateral influences come to mind in looking at Buff's works. One is the woodblock prints associated with the Jungendstil and Art Nouveau movements in Europe, and the Japanese woodblock. The stark contrasts and the rectilinearity of the works is almost draftsmanlike in approach. In Mountains by the Lakeside those parallels are clearly evident in the treatment of the white mountains, the lake and the trees.
Buff utilized broad brushstrokes and at times a pointillisme. There is an opacity that anticipates acrylic. Utilizing oil on board, he sketched directly onto the board, conceiving the painting as one with the sketch.
Even though landscapes predominate in his work, Buff also turned his attention to still lifes and portraits, including self-portraits, revealing studies of passing time, of a face as craggy and strong as the landscape he immortalized.


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For all your solid wood flooring requirements, please feel free to contact out trained support team or feel free to view our extensive engineered wood flooring range.


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Rent your own mountain for a ski day. Seriously
Carrie Tait
Libby, mont.
This article was published more than 11 years ago. Some information may no longer be current.
Turner Mountain - taking a break in the mountain top snow pit.
It was ours, all ours.
Turner Mountain was coated with more than 40 centimetres of fresh powder when our busload of friends rolled into its tiny parking lot in February. The virgin white bounty tucked away in Montana's backwoods was untouched: No people on the chairlift. No tracks on the slopes. No one could slash the snowy perfection until we said so. We were in charge.
It was, for one glorious day, our private playground. We rented the mountain – all 240 skiable hectares of it. We wore scratched helmets and knit tuques, but weighty gold crowns and delicate diamond tiaras would have felt right too. Because, seriously, who rents a mountain? A mountain complete with lifties and ski patrol to keep everyone safe, a lodge to keep everyone warm, and cook staff to keep everyone fed. As we poured out of our chariot that nippy morning, we asked a more relevant question: For $45 each, which is cheaper than a lift ticket at many lesser mountains, why doesn't everyone do this?
There was a short moment of silence when we arrived, as we stared out the bus window, blown away by our luck. But this was no time for deep thoughts – we had shredding to do. There was a rush to tie up snowboard boot laces, clamp down ski-boot snaps and get to the top. I was among the first on the chairlift, and while looking down at the wispy powder, I did what everyone else did: hooted, hollered, giggled and squirmed in my seat, wishing the slowest chairlift in the world would go just a touch faster.
"All I can say is: 'Wow, man,'" said Chris Gallagher, one of the trip's organizers. "I've never seen so much fresh powder spread amongst so few people for so cheap."
From the top of the mountain, Turner's perfection – it looked like a brand-new, white, poufy goose-down duvet stretching 643 vertical metres – was second only to its eeriness. This kind of emptiness is usually reserved for those who go to the trouble of skinning mountains in the lonesome backcountry, or cough up a grand for a day of heli-skiing. All we had to do was ride a rickety double chairlift.
But – again! – enough dawdling. It was time to rip.
I scooched my snowboard to the edge of Sundance Bowl, one of Turner's 25 open runs, and glided right. Riding powder is like floating on unbelievably airy whipped cream – soft, fluffy and free. Wiping out in the stuff is just as much of a treat, save for the unplanned front flip that usually comes with my powder crashes. Both are equally fun, although my spectacular acrobatics drew more applause from the swinging chairlift above.
Luckily, egos go unscathed on days when you're playing in snow up to your knees. It takes a run or two to remember how to survive in powder. You have to shift your weight to the tail of your board to keep snow from accumulating on the nose, which weighs you down and ends in a face plant.
I raced down the mountain a few times – endlessly repeating the hoot, float, crash sequence – before the beauty of renting a mountain finally sank in: I didn't have to hustle to be the first through a swath of trees or to cut a fresh line on a run that already had visitors. There were so few of us, the day was almost over before making a run involved the burden of crossing over someone else's tracks.
Turner Mountain, which celebrated its 50th anniversary this past winter, is a secluded secret in the northwest corner of Montana. Libby, the nearest notable town, is home to about 2,600 people. Despite its pristine surroundings, the town is best known for its asbestos crisis and subsequent lawsuits. The U.S. Environmental Protection Agency has been cleaning up the mess since 1999.
Geography, and perhaps the area's modest incomes, keeps the ski area quiet – Turner is 177 kilometres from Kalispell, Mont., making airport access tricky, particularly when the larger, glitzy Whitefish Mountain Resort is right there. Going the other direction, Spokane, Wash., is about 290 clicks away, and that means driving by Schweitzer Mountain Resort. We travelled about 265 kilometres from Kimberley, B.C., to get to Turner, again bypassing bigger ski areas.
The mountain's isolation means it is open only Friday, Saturday and Sunday, but the non-profit organization will spin the lifts any other day in exchange for the rental fee. We paid $2,500 (U.S.) last year, and donated $400 to a fundraising drive to buy a new grooming machine. It will cost $3,000 for the privilege of privacy in 2012. We've booked our trip.
Renting the mountain on Thursday means any snow that fell the previous three days is part of the deal, but piles of powder are not guaranteed. I've been on a private excursion to Turner when spring conditions dominated the mountain; there was no need to pout. Rather than drinking cases of beer and playing games inside, as we did on our snow day when tingling frostbite trumped pleasurable powder, we drank kegs of beer while dancing in T-shirts outside as a DJ cranked the music.
"There are no rules at Turner," said Don Crawford, who has volunteered at the mountain since the 1970s. "We just really enjoy sharing the mountain with people who enjoy skiing. It is a special place. There's not a lot like it."
He's not exaggerating. Lifties will help you load cases of beer onto the chairlift – try doing that at Whistler – and, for those with lesser skills, they'll slow it down at the top so you can safely unload the cargo. On our second day at Turner, when we had to share the mountain with two dozen or so commoners, the folks running the mountain pushed snow at the summit into a three-metre-high ring and let us carve out a sunpit. At their best, sunpits look like Roman amphitheatres, with friends stacked thick and high on the bleachers, a fire burning in the centre, and beer chilling in the snow. Think of them as forts for adults. The 1.6-kilometre-long chairlift, which thankfully replaced Turner's old T-bar, shut down before we left the sunpit Friday (a rule violation at any other mountain). Don, who brought diesel fuel and firewood to power our party, eventually herded our rat pack down the mountain on mellow terrain. "Be careful," he said. The warning, delivered with a laugh yet without jest, was fair. Careful is not among our guiding principles.
We may act like ski bums, but we have real jobs, with geologists, pharmacists, an acupuncturist and a chief executive officer among us. Just goes to show you, anyone can rent a mountain.
Follow Carrie Tait on Twitter: @CarrieTaitOpens in a new window
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