def get_subembed(self, word_list, normalize=False, restrict_context=True):
"""
Gets subembedding.
"""
w_set = set(self.iw)
valid_w = [word for word in word_list if word in w_set]
new_w_indices = np.array([self.wi[word] for word in valid_w])
if restrict_context:
c_set = set(self.ic)
valid_c = [word for word in word_list if word in c_set]
new_c_indices = np.array([self.ci[word] for word in valid_c])
new_m = self.m[new_w_indices, :]
new_m = new_m[:, new_c_indices]
else:
valid_c = self.ic
new_m = self.m[new_w_indices, :]
return Explicit(new_m, valid_w, valid_c, normalize=normalize)
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