How to use the miscnn.utils.patch_operations.slice_matrix function in miscnn

def analysis_patchwise_grid(self, sample, training, data_aug):
        # Slice image into patches
        patches_img = slice_matrix(sample.img_data, self.patch_shape,
                                   self.patchwise_overlap,
                                   self.data_io.interface.three_dim)
        if training:
            # Slice segmentation into patches
            patches_seg = slice_matrix(sample.seg_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
        else : patches_seg = None
        # Skip blank patches (only background)
        if training and self.patchwise_skip_blanks:
            # Iterate over each patch
            for i in reversed(range(0, len(patches_seg))):
                # IF patch DON'T contain any non background class -> remove it
                if not np.any(patches_seg[i][...,self.patchwise_skip_class] != 1):
                    del patches_img[i]
                    del patches_seg[i]
        # Concatenate a list of patches into a single numpy array
        img_data = np.stack(patches_img, axis=0)
        if training : seg_data = np.stack(patches_seg, axis=0)
        # Pad patches if necessary
        if img_data.shape[1:-1] != self.patch_shape and training:

def analysis_patchwise_crop(self, sample, data_aug):
        # If skipping blank patches is active
        if self.patchwise_skip_blanks:
            # Slice image and segmentation into patches
            patches_img = slice_matrix(sample.img_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
            patches_seg = slice_matrix(sample.seg_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
            # Skip blank patches (only background)
            for i in reversed(range(0, len(patches_seg))):
                # IF patch DON'T contain any non background class -> remove it
                if not np.any(patches_seg[i][...,self.patchwise_skip_class] != 1):
                    del patches_img[i]
                    del patches_seg[i]
            # Select a random patch
            pointer = np.random.randint(0, len(patches_img))
            img = patches_img[pointer]
            seg = patches_seg[pointer]
            # Expand image dimension to simulate a batch with one image

def analysis_patchwise_crop(self, sample, data_aug):
        # If skipping blank patches is active
        if self.patchwise_skip_blanks:
            # Slice image and segmentation into patches
            patches_img = slice_matrix(sample.img_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
            patches_seg = slice_matrix(sample.seg_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
            # Skip blank patches (only background)
            for i in reversed(range(0, len(patches_seg))):
                # IF patch DON'T contain any non background class -> remove it
                if not np.any(patches_seg[i][...,self.patchwise_skip_class] != 1):
                    del patches_img[i]
                    del patches_seg[i]
            # Select a random patch
            pointer = np.random.randint(0, len(patches_img))
            img = patches_img[pointer]
            seg = patches_seg[pointer]
            # Expand image dimension to simulate a batch with one image
            img_data = np.expand_dims(img, axis=0)
            seg_data = np.expand_dims(seg, axis=0)
            # Pad patches if necessary

def analysis_patchwise_grid(self, sample, training, data_aug):
        # Slice image into patches
        patches_img = slice_matrix(sample.img_data, self.patch_shape,
                                   self.patchwise_overlap,
                                   self.data_io.interface.three_dim)
        if training:
            # Slice segmentation into patches
            patches_seg = slice_matrix(sample.seg_data, self.patch_shape,
                                       self.patchwise_overlap,
                                       self.data_io.interface.three_dim)
        else : patches_seg = None
        # Skip blank patches (only background)
        if training and self.patchwise_skip_blanks:
            # Iterate over each patch
            for i in reversed(range(0, len(patches_seg))):
                # IF patch DON'T contain any non background class -> remove it
                if not np.any(patches_seg[i][...,self.patchwise_skip_class] != 1):
                    del patches_img[i]
                    del patches_seg[i]