There are some common misuse patterns in TorchScript that we should issue clear error messages for instead of generating generic error that doesn't capture root cause of error.

Here are a few examples:

• Attempting to construct a nn.Module inside TorchScript. This currently errors out because TorchScript would attempt to compile __init__() method of module, which usually contains a call

Picking up from #15731, there are many places in numpy where we do something like:

try:
    something_which_throws_typeError
except TypeError:
    raise ValueError("a clearer explanation of what went wrong")

It would produce a marginally clearer error if we could change these to use traceback chaining. Our two options are either:

1. The inner error provides valuable infor

I just ran into an issue when trying to use to_csv with distributed workers that don't share a file system. I shouldn't have been surprised that writing to a local file system from a distributed worker doesn't work. It shouldn't work. But the error I got was just a File Not Found error. That brought me to:dask/dask#2656 (comment) - which was the answer.

It's normal to have many map() calls, and some of them can take a few minutes,
it would be nice to have a description on the progress bar.

Alternative solution:
Print the description before/after the map() call.

In numba/stencils/stencil.py, there are various places (like line 552, "if isinstance(kernel_size[i][0], int):") where we check for "int" in relation to neighborhoods. I ran across a case where I was creating a neighborhood tuple by extracting values from a Numpy array. This causes a problem because those Numpy values will not match in these isinstance int checks. I worked around it by conver

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

• What's your use case?

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

hi,
if possible, please add these indicators as well:
TDI (Traders Dynamic Index)
chandelier exit
pivot points
BOP (balance of power)
CTM (Chande trend meter)
Coppock Curve
Correlation Coefficient
PMO (DecisionPoint Price Momentum Oscillator)
Ulcer Index
most of them except TDI are available on stockcharts.com
thanks

Implements {DataFrame,Series}.empty