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i think you should add just condition for handling when minutes is greater than 59 and when hour is greater than 12.

You can use Sliver widget instead of using ListView.builder. ListVew wideget will build all widget at once, whereas by using Sliver widget you can build widgets when user scroll the screen. Refer the sample code below.

I've solved my own problem by adding these lines of code inside the for loop.

for i in range(len(scores)):

Here's one way in which this can be done.

You basically inject the "sheet" name of your excel spreadsheet into the current tag's context viz., ITestContext as an attribute and then from within the @DataProvider annotated data provider, you basically read this attribute to decide which sheet has to be read.

The below sample demonstrates two @Test methods doing this, wherein the first @Test method injects this attribute as part of it doing a flow and the second @Test method (it would have to depend on the first one) which is now powered by a dynamic data provider just consumes the data.

This is how I would do it: I would save the glyphs as symbols. Please observe that the symbols have a viewBox attribute. This is allowing me to resize the glyphs as needed.

Also I've transformed the paths transform="scale(1,-1) translate(0,-1700)".

Thank you very much to @MoshFeu for this comment:

It should be rotated like this:i.stack.imgur.com/oC6qn.png – Mosh Feu

I'm saving the symbols in an svg element with width="0" height="0" and position:absolute;. This way the svg element with the definitions is not interfering in the layout. Now you can use the glyphs with a element giving them the size and the position you need:

I need to perform Union of tables 2018,2017,2016 without any duplicate emails minus table 2019

The most easy to simulate/emulate minus/expect is using NOT IN()

Query

I just couldn't handle the old code. One based functions? No! You can translate it back it you want but output is now dog.

You should add merged value in your run function:

Problem is your network is having many layers and also the depth(number of filters) of each layer is very high. Also, you are training the network from scratch. And your dataset of MNIST (60000 images) is very little. Moreover, each MNIST image is only 28x28x1 size.

Couple of alternatives what I can suggest you is to retrain a pre-trained model, i.e do transfer learning. Look into this AlexNet weight file. The architecture is slightly different from what you have done in your code. Good thing about this way of implementation is, it will offset for very little data you have.

Other better alternative is to reduce the number of layers and number of filters in each layer. In this way, you will be able to train the model from scratch and also it will be very fast. (Assuming you don't have GPU). Take a look at LeNet-5 architecture.

Hope the answer helps you.