# Buggy code
def stack(data, parallel_processing, dtype):
    """Stack a list of data.
    Used when creating a single batch from list of dicts
    depending on whether multiprocessing is turned on, the batches will be
    constructed using different memory allocation techniques"""
    if isinstance(data[0], mx.nd.NDArray):
        if parallel_processing:
            out = nd.empty(
                (len(data),) + data[0].shape,
                dtype=data[0].dtype,
                ctx=context.Context("cpu_shared", 0),
            )
            return mx.nd.stack(*data, out=out)
        else:
            return mx.nd.stack(*data)
    elif isinstance(data[0], np.ndarray):
        data = np.asarray(data)
        if data.dtype.kind == "f":
            data = data.astype(dtype)
        if parallel_processing:
            return mx.nd.array(
                data, dtype=data.dtype, ctx=context.Context("cpu_shared", 0)
            )
        else:
            return mx.nd.array(data, dtype=data.dtype)
    elif isinstance(data[0], list):
        return list(stack(t, parallel_processing, dtype) for t in zip(*data))
    elif isinstance(data[0], tuple):
        return tuple(stack(t, parallel_processing, dtype) for t in zip(*data))
    elif isinstance(data[0], (pd.Timestamp, str, int, pathlib.PosixPath)):
        return data
    else:
        raise TypeError(
            f"Invalid type of data: {type(data[0])} for argument loss_function."
        )


# 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 stack(
    data,
    multi_processing: bool,
    dtype: DType,
    single_process_ctx: Optional[mx.Context] = None,
):
    """Stack a list of data.
    Used when creating a single batch from list of dicts
    depending on whether multiprocessing is turned on, the batches will be
    constructed using different memory allocation techniques"""
    if isinstance(data[0], mx.nd.NDArray):
        if multi_processing:
            out = nd.empty(
                (len(data),) + data[0].shape,
                dtype=data[0].dtype,
                ctx=context.Context("cpu_shared", 0),
            )
            return mx.nd.stack(*data, out=out)
        else:
            return mx.nd.stack(*data)
    elif isinstance(data[0], np.ndarray):
        data = np.asarray(data)
        if data.dtype.kind == "f":
            data = data.astype(dtype)
        if multi_processing:
            # Workaround due to MXNet not being able to handle NDArrays with 0 in shape properly:
            if 0 in data.shape:
                return data
            return mx.nd.array(
                data, dtype=data.dtype, ctx=context.Context("cpu_shared", 0)
            )
        else:
            return mx.nd.array(data, dtype=data.dtype, ctx=single_process_ctx)
    elif isinstance(data[0], list):
        return list(
            stack(t, multi_processing, dtype, single_process_ctx) for t in zip(*data)
        )
    elif isinstance(data[0], tuple):
        return tuple(
            stack(t, multi_processing, dtype, single_process_ctx) for t in zip(*data)
        )
    elif isinstance(data[0], (pd.Timestamp, str, int, pathlib.PosixPath)):
        return data
    else:
        raise TypeError(
            f"Invalid type of data: {type(data[0])} for argument loss_function."
        )