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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4:  Implement a custom pytoch Dataset to load image frames from a remote DICOM VL Whole Slide Microscopy Image instance
Step6:  Implement a simple binary image segmentation model
Step7:  Instantiate the Dataset, pass inputs to the model and receive back outputs in form of NumPy arrays
Step8:  Plot an overview of model inputs and outputs
Step9:  Encode model outputs in form of a DICOM Segmentation instance
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Python Code:
class Dataset(torch.utils.data.Dataset):
    
    Class for getting individual Pixel Data element frame items of a DICOM VL Whole Slide Microscocpy Image data set stored on a remote server.
    def __init__(self, url: str, study_id: str, series_id: str, instance_id: str):
        
        Parameters
        ----------
        url: str
            Address of a DICOMweb origin server
        study_id: str
            Study Instance UID
        series_id: str
            Seriess Instance UID of a Slide Microscopy series
        instance_id: str
            SOP Instance UID of a VL Whole Slide Microscopy Image instance
        
        self.client = dicomweb_client.api.DICOMwebClient(url)
        metadata = self.client.retrieve_instance_metadata(
            study_instance_uid=study_id,
            series_instance_uid=series_id,
            sop_instance_uid=instance_id
        )
        self.meta = pydicom.dataset.Dataset.from_json(metadata)
    def __len__(self) -> int:
        int: number of frames
        return int(self.meta.NumberOfFrames)
    def __getitem__(self, idx: int) -> numpy.ndarray:
        Retrieves an invidivual frame.
        
        Parameters
        ----------
        idx: int
            Zero-based frame index
            
        Returns
        -------
        numpy.ndarray
            Pixels of the frame
            
        
        frames = self.client.retrieve_instance_frames(
            study_instance_uid=self.meta.StudyInstanceUID,
            series_instance_uid=self.meta.SeriesInstanceUID,
            sop_instance_uid=self.meta.SOPInstanceUID,
            frame_numbers=[idx+1],
            media_types=('image/jpeg', 'image/jp2', )
        )
        buf = io.BytesIO(frames[0])
        return numpy.array(PIL.Image.open(buf))
Explanation: Implement a custom pytoch Dataset to load image frames from a remote DICOM VL Whole Slide Microscopy Image instance
End of explanation
def model(image: numpy.ndarray) -> numpy.ndarray:
    Segments a microscopy image into regions representing tissue foreground and slide background.
    
    Parameters
    ----------
    image: numpy.ndarray
        Pixel matrix of an image or image frame
        
    Returns
    -------
    numpy.ndarray
        Binary mask where tissue foreground is ``True`` and slide background is ``False``
    
    
    return numpy.max(image < 225, 2).astype(numpy.bool)
Explanation: Implement a simple binary image segmentation model
End of explanation
dataset = Dataset(
    url='https://server.dcmjs.org/dcm4chee-arc/aets/DCM4CHEE/rs',
    study_id='1.2.392.200140.2.1.1.1.2.799008771.3960.1519719403.819',
    series_id='1.2.392.200140.2.1.1.1.3.799008771.3960.1519719403.820',
    instance_id='1.2.392.200140.2.1.1.1.4.799008771.3960.1519719570.834'
)
inputs = []
outputs = []
for i in range(len(dataset)):
    image_frame = dataset[i]
    mask_frame = model(image_frame)
    inputs.append(image_frame)
    outputs.append(mask_frame)
image = numpy.stack(inputs)
mask = numpy.stack(outputs)
print('Expected input shape  : ', (int(dataset.meta.NumberOfFrames), dataset.meta.Rows, dataset.meta.Columns, dataset.meta.SamplesPerPixel))
print('Actual input shape    : ', image.shape)
print('Expected output shape : ', (int(dataset.meta.NumberOfFrames), dataset.meta.Rows, dataset.meta.Columns))
print('Actual ouput shape    : ', mask.shape)
Explanation: Instantiate the Dataset, pass inputs to the model and receive back outputs in form of NumPy arrays
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fig, axs = matplotlib.pyplot.subplots(
    nrows=10,
    ncols=len(dataset) // 5,
    figsize=(10, 10),
    subplot_kw={'xticks': [], 'yticks': []}
)
for i, ax in enumerate(axs.flat[:(len(axs.flat) // 2)]):
    ax.imshow(image[i])
for i, ax in enumerate(axs.flat[(len(axs.flat) // 2):]):
    ax.imshow(mask[i].astype(numpy.uint8) * 255)
    
matplotlib.pyplot.tight_layout()
Explanation: Plot an overview of model inputs and outputs
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algorithm = highdicom.content.AlgorithmIdentificationSequence(
    name='Binary Image Segmentation Example',
    family=pydicom.sr.codedict.codes.cid7162.ArtificialIntelligence,
    version='v0.1.0'
)
segment_description = highdicom.seg.content.SegmentDescription(
    segment_number=1,
    segment_label='ROI #1',
    segmented_property_category=pydicom.sr.codedict.codes.cid7150.Tissue,
    segmented_property_type=pydicom.sr.codedict.codes.SCT.BodyFat,
    algorithm_type=highdicom.seg.enum.SegmentAlgorithmTypeValues.AUTOMATIC,
    algorithm_identification=algorithm
)
segmentation = highdicom.seg.sop.Segmentation(
    source_images=[dataset.meta],
    pixel_array=mask,
    segmentation_type=highdicom.seg.enum.SegmentationTypeValues.FRACTIONAL,
    segment_descriptions=[segment_description],
    series_instance_uid=highdicom.uid.UID(),
    series_number=2,
    sop_instance_uid=highdicom.uid.UID(),
    instance_number=1,
    manufacturer='MGH Computational Pathology',
    manufacturer_model_name='Example Jupyter Notebook',
    software_versions=highdicom.version.__version__,
    device_serial_number='XXX'
)
print(segmentation)
Explanation: Encode model outputs in form of a DICOM Segmentation instance
End of explanation
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