Replies: 3 comments 4 replies
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I'm not sure you are calling the function properly (even though I haven't seen your code). Here is the proper way to do this. Just enter in your semantic model name and workspace name in the corresponding parameters. You can also specify a measure or list of measures using the 'measure_name' parameter within the generate_measure_descriptions function. from sempy_labs.tom import connect_semantic_model
with connect_semantic_model(dataset='', workspace='', readonly=False) as tom:
tom.generate_measure_descriptions() |
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You need to run this on a paid F64 sku or higher. |
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Hmm, interesting. I'll have to check and get back to you - likely after the holidays. |
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Hi,
I've firstly run the module "connect_semantic_model" in order to connect to a semantic model and being able to use TOM package.
After that, I've run the "labs.tom.TOMWrapper.generate_measure_descriptions" command but obtained this error:
TypeError: TOMWrapper.generate_measure_descriptions() missing 1 required positional argument: 'self'
What is the missing argument? Based on documentation, no argument is mandatory (measure_name and max_batch_size are optional)
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