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I think the main misunderstanding is that my Int @a = 1,2,3 and [1,2,3] are somehow equivalent. They are not. The first case defines an array that will only take Int values. The second case defines an array that will take anything, and just happens to have Int values in it.

I'll try to cover all versions you tried, why they didn't work, and possibly how it would work. I'll be using a bare dd as proof that the body of the function was reached.

#1

You can use ngif to check the condition and show the data:

try via index attribute and to_datetime() method:

A "for expression" must return unit, so the result is not propagated. For example:

It varies between implementations, but generally, this is managed by the cache coherency protocol of the multiprocessor. In simplest terms, what happens is that when CPU1 writes to a memory location, that location will be invalidated in every other cache in the system. So that write will invalidate the line in CPU2's instruction cache as well as any (partially) decoded instructions in CPU2's uop cache (if it has such a thing). So when CPU2 goes to fetch/execute the next instruction, all those caches will miss and it will stall while things are refetched. Depending on the cache coherency protocol, that may involve waiting for the write to get to memory, or may fetch the modified data directly from CPU1's dcache, or things might go via some shared cache.

Try this,

• You can pass warehouse_name as default parameter.
• Make a conditional call to file_name_for_non_duplicate and logger.info.

You are looking for ():

You need to define a different surrogate posterior. In Tensorflow's Bayesian linear regression example https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_Regression.ipynb#scrollTo=VwzbWw3_CQ2z

you have the posterior mean field as such

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.