def label$\_$vectorizer$($labels$,$ tag$\_$map$)$:

$"""$

Convert list of label strings to padded label IDs using a tag mapping.

Parameters:

labels $($list of str$)$: List of label strings.

tag$\_$map $($dict$)$: Dictionary mapping tags to IDs.

Returns:

label$\_$ids $($numpy$.$ndarray$)$: Padded array of label IDs.

$"""$

label$\_$ids $=[]$ # It can't be a numpy array yet, since each sentence has a different size

### START CODE HERE ###

# Each element in labels is a string of tags so for each of them:

for element in labels:

# Split it into single tokens. You may use $.$split function for strings. Be aware to split it by a blank space!

tokens $=$ element.split$("")$

#print$($tokens$)$

#print$($tag$\_$map$)$

# Use the dictionaty tag$\_$map passed as an argument to the label$\_$vectorizer function

# to make the correspondence between tags and numbers.

element$\_$ids $=[$ tag$\_$map.get$($item$,$item$)$ for item in tokens $]$

# for token in tokens:

# element$\_$ids.append$($None$)$

# Append the found ids to corresponding to the current element to label$\_$ids list

# label$\_$ids.append$($None$)$

label$\_$ids $=$ np$.$array$($element$\_$ids$)$

print$($label$\_$ids$)$

# Pad the elements

label$\_$ids $=$ tf$.$keras.utils.pad$\_$sequences$($

sequences$=$labels,

maxlen$=$None,

dtype$=$'int$32\text{'},$

padding$=$'post',

truncating$=$'pre',

value$=0\mathrm{.}0$

$)$

### END CODE HERE ###

return label$\_$ids