# Buggy code
def from_bytes(self, bytes_data, disable=[]):
    """Load state from a binary string.

    bytes_data (bytes): The data to load from.
    disable (list): Names of the pipeline components to disable.
    RETURNS (Language): The `Language` object.
    """
    deserializers = OrderedDict(
        (
            ("vocab", lambda b: self.vocab.from_bytes(b)),
            ("tokenizer", lambda b: self.tokenizer.from_bytes(b, vocab=False)),
            ("meta", lambda b: self.meta.update(ujson.loads(b))),
        )
    )
    for i, (name, proc) in enumerate(self.pipeline):
        if name in disable:
            continue
        if not hasattr(proc, "from_bytes"):
            continue
        deserializers[i] = lambda b, proc=proc: proc.from_bytes(b, vocab=False)
    msg = util.from_bytes(bytes_data, deserializers, {})
    return self


# Traceback (ValueError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Projects/foobar/.env/lib/python3.6/site-packages/spacy/language.py", line 333, in __call__
doc = proc(doc)
File "pipeline.pyx", line 390, in spacy.pipeline.Tagger.__call__
File "pipeline.pyx", line 402, in spacy.pipeline.Tagger.predict
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
return self.predict(x)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 55, in predict
X = layer(X)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
return self.predict(x)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 293, in predict
X = layer(layer.ops.flatten(seqs_in, pad=pad))
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
return self.predict(x)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 55, in predict
X = layer(X)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
return self.predict(x)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/model.py", line 125, in predict
y, _ = self.begin_update(X)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 372, in uniqued_fwd
Y_uniq, bp_Y_uniq = layer.begin_update(X[ind], drop=drop)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 61, in begin_update
X, inc_layer_grad = layer.begin_update(X, drop=drop)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in begin_update
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in <listcomp>
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 258, in wrap
output = func(*args, **kwargs)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in begin_update
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in <listcomp>
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 258, in wrap
output = func(*args, **kwargs)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in begin_update
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in <listcomp>
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 258, in wrap
output = func(*args, **kwargs)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in begin_update
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 176, in <listcomp>
values = [fwd(X, *a, **k) for fwd in forward]
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/api.py", line 258, in wrap
output = func(*args, **kwargs)
File "/Projects/foobar/.env/lib/python3.6/site-packages/thinc/neural/_classes/static_vectors.py", line 67, in begin_update
dotted = self.ops.batch_dot(vectors, self.W)
File "ops.pyx", line 299, in thinc.neural.ops.NumpyOps.batch_dot
ValueError: shapes (4,0) and (300,128) not aligned: 0 (dim 1) != 300 (dim 0)

# Fixed code
def from_bytes(self, bytes_data, disable=[]):
    """Load state from a binary string.

    bytes_data (bytes): The data to load from.
    disable (list): Names of the pipeline components to disable.
    RETURNS (Language): The `Language` object.
    """
    deserializers = OrderedDict(
        (
            ("vocab", lambda b: self.vocab.from_bytes(b)),
            ("tokenizer", lambda b: self.tokenizer.from_bytes(b, vocab=False)),
            ("meta", lambda b: self.meta.update(ujson.loads(b))),
        )
    )
    _fix_pretrained_vectors_name(self)
    for i, (name, proc) in enumerate(self.pipeline):
        if name in disable:
            continue
        if not hasattr(proc, "from_bytes"):
            continue
        deserializers[i] = lambda b, proc=proc: proc.from_bytes(b, vocab=False)
    msg = util.from_bytes(bytes_data, deserializers, {})
    return self