Feature Request: Pipelining Outlier Removal · Issue #9630 · scikit-learn/scikit-learn
Yes, #3855, #4143, #4552 and scikit-learn/enhancement_proposals#2 all relate.

I don't see why resample is entirely inappropriate here.

Meta-estimator (almost; untested):

class WithoutOutliersClassifier(BaseEstimator, ClassifierMixin):
def __init__(self, outlier_detector, classifier):
self.outlier_detector = outlier_detector
self.classifier = classifier

def fit(self, X, y):
self.outlier_detector_ = clone(self.outlier_detector)
mask = self.outlier_detector_.fit_predict(X, y) == 1
self.classifier_ = clone(self.classifier).fit(X[mask], y[mask])
return self

def predict(self, X):
return self.classifier_.predict(X)
sklearn  pipeline  meta  estimator  transform  target  sample 
january 2019
micro:bit IoT In C - First Steps With The GPIO
This is complicated so a summary will help:

In input mode you only have to keep the voltage on a GPIO line between 0 and 3.3V. The GPIO line in input mode is high impedance and draws little current from whatever is connected to it.
In output mode with the default standard drive the voltage is between 0 and 3.3V and the current has to be less than 0.5mA
In output mode a GPIO line with high drive turned on the current has to be less than 5mA.
The total current in all of the GPIO lines has to be less than 15mA.
micro  bit  gpio  maximum  ratings 
december 2018
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