# Buggy code
def _worker_fn(
    batch_size: int,
    batchify_fn: Callable,
    dtype: DType,
    is_train: bool,
    shuffle: bool,
    cyclic: bool,
    cycle_num: int,
):
    """Function for processing data in worker process."""

    global _worker_dataset_iterator
    global _worker_iterator_latest_reset_cycle
    global _worker_iterator_exhausted_indicator

    # initialize, or reset the iterator at each cycle
    assert isinstance(_worker_iterator_latest_reset_cycle, int)
    if (_worker_iterator_latest_reset_cycle < cycle_num) and (
        _worker_iterator_latest_reset_cycle == 0 or not cyclic
    ):
        _worker_reset_iterator(is_train, cyclic, cycle_num)

    assert isinstance(_worker_dataset_iterator, Iterable), (
        f"Dataset not Iterable: {type(_worker_dataset_iterator)}."
    )
    transformed_data = list(itertools.islice(_worker_dataset_iterator, batch_size))

    if shuffle:
        random.shuffle(transformed_data)

    if transformed_data:
        success = True
        batch = batchify_fn(
            data=transformed_data, dtype=dtype, parallel_processing=True
        )
    else:
        # the second time without being able to provide a batch we want to delay calling them again
        # on fist exhaustion they should not be delayed, since they need to indicate depletion
        if _worker_iterator_exhausted_indicator:
            time.sleep(0.1)
        else:
            _worker_iterator_exhausted_indicator = True
        success = False
        batch = None

    buf = io.BytesIO()
    ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(
        (success, MPWorkerInfo.worker_id, batch)
    )
    return buf.getvalue()


# Traceback (mxnet.base.MXNetError)
Traceback (most recent call last):
File "/usr/local/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/ml/code/train.py", line 137, in <module>
forecasts, tss, agg_metrics, item_metrics = evaluate(predictor, dataset.test)
File "/opt/ml/code/utils.py", line 30, in evaluate
forecasts = list(forecast_it)
File "/usr/local/lib/python3.6/site-packages/gluonts/model/predictor.py", line 317, in predict
num_samples=num_samples,
File "/usr/local/lib/python3.6/site-packages/gluonts/model/forecast_generator.py", line 195, in __call__
for batch in inference_data_loader:
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/loader.py", line 101, in __iter__
yield from self.parallel_data_loader
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/parallelized_loader.py", line 572, in same_process_iter
for k, v in batch.items()
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/parallelized_loader.py", line 572, in <dictcomp>
for k, v in batch.items()
File "/usr/local/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 2765, in as_in_context
return self.copyto(context)
File "/usr/local/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 2632, in copyto
return _internal._copyto(self, out=hret)
File "<string>", line 27, in _copyto
File "/usr/local/lib/python3.6/site-packages/mxnet/_ctypes/ndarray.py", line 107, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/usr/local/lib/python3.6/site-packages/mxnet/base.py", line 255, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [18:26:25] src/imperative/./imperative_utils.h:146: Operator _copyto inferring shapes failed.
input shapes:
[1,321,-1]
output shapes:
[1,321,-1]
operator attributes:

Stack trace:
[bt] (0) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x6dfb0b) [0x7f99a9e28b0b]
[bt] (1) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::imperative::SetShapeType(mxnet::Context const&amp;, nnvm::NodeAttrs const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, mxnet::DispatchMode*)+0x363b) [0x7f99ad0da57b]
[bt] (2) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context const&amp;, nnvm::NodeAttrs const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;)+0x1db) [0x7f99ad0e24bb]
[bt] (3) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x3847f3f) [0x7f99acf90f3f]
[bt] (4) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(MXImperativeInvokeEx+0x62) [0x7f99acf91502]
[bt] (5) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(ffi_call_unix64+0x4c) [0x7f9a01ccf4e6]
[bt] (6) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(ffi_call+0x3f1) [0x7f9a01cce241]
[bt] (7) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2cf) [0x7f9a01cc58ff]
[2020-04-02 18:26:25.315 ip-10-0-168-31.us-west-2.compute.internal:213 INFO utils.py:25] The end of training job file will not be written for jobs running under SageMaker.
[bt] (8) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x9829) [0x7f9a01cbc829]

# Fixed code
def _worker_fn(
    batch_size: int,
    batchify_fn: Callable,
    dtype: DType,
    is_train: bool,
    shuffle: bool,
    cyclic: bool,
    cycle_num: int,
):
    """Function for processing data in worker process."""

    # initialize, or reset the iterator at each cycle
    assert isinstance(_WorkerData.iterator_latest_reset_cycle, int)
    if (_WorkerData.iterator_latest_reset_cycle < cycle_num) and (
        _WorkerData.iterator_latest_reset_cycle == 0 or not cyclic
    ):
        _worker_reset_iterator(is_train, cyclic, cycle_num)

    assert isinstance(_WorkerData.dataset_iterator, Iterable), (
        f"Dataset not Iterable: {type(_WorkerData.dataset_iterator)}."
    )
    transformed_data = list(itertools.islice(_WorkerData.dataset_iterator, batch_size))

    if shuffle:
        random.shuffle(transformed_data)

    if transformed_data:
        success = True
        batch = batchify_fn(data=transformed_data, dtype=dtype, multi_processing=True)
    else:
        # the second time without being able to provide a batch we want to delay calling them again
        # on fist exhaustion they should not be delayed, since they need to indicate depletion
        if _WorkerData.iterator_exhausted_indicator:
            time.sleep(0.1)
        else:
            _WorkerData.iterator_exhausted_indicator = True
        success = False
        batch = None

    buf = io.BytesIO()
    ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(
        (success, MPWorkerInfo.worker_id, batch)
    )
    return buf.getvalue()