How to use the datasets.dataset_utils.float_feature function in datasets

'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_0': dataset_utils.int64_feature(pose_mask_r7_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_1': dataset_utils.int64_feature(pose_mask_r7_1.astype(np.int64).flatten().tolist()),
            'seg_0': dataset_utils.int64_feature(seg_0.astype(np.int64).flatten().tolist()),
            'seg_1': dataset_utils.int64_feature(seg_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),
            
            'indices_r4_0': dataset_utils.int64_feature(np.array(indices_r4_0).astype(np.int64).flatten().tolist()),
            'values_r4_0': dataset_utils.float_feature(np.array(values_r4_0).astype(np.float).flatten().tolist()),
            'indices_r4_1': dataset_utils.int64_feature(np.array(indices_r4_1).astype(np.int64).flatten().tolist()),
            'values_r4_1': dataset_utils.float_feature(np.array(values_r4_1).astype(np.float).flatten().tolist()),

            'pose_subs_0': dataset_utils.float_feature(pose_subs_0),
            'pose_subs_1': dataset_utils.float_feature(pose_subs_1),

            'part_bbox_0': dataset_utils.int64_feature(np.array(part_bbox_list_0).astype(np.int64).flatten().tolist()),
            'part_bbox_1': dataset_utils.int64_feature(np.array(part_bbox_list_1).astype(np.int64).flatten().tolist()),
            'part_vis_0': dataset_utils.int64_feature(np.array(visibility_list_0).astype(np.int64).flatten().tolist()),
            'part_vis_1': dataset_utils.int64_feature(np.array(visibility_list_1).astype(np.int64).flatten().tolist()),
    }))

    return example

ddd_bbox_w_list.append(ddd_bbox_w)
          ddd_bbox_l_list.append(ddd_bbox_l)
          ddd_bbox_x_list.append(ddd_bbox_x)
          ddd_bbox_y_list.append(ddd_bbox_y)
          ddd_bbox_z_list.append(ddd_bbox_z)
          ddd_bbox_ry_list.append(ddd_bbox_ry)

    image_format = b'PNG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/object/bbox/xmin': float_feature(bbox_x1_list),
            'image/object/bbox/xmax': float_feature(bbox_x2_list),
            'image/object/bbox/ymin': float_feature(bbox_y1_list),
            'image/object/bbox/ymax': float_feature(bbox_y2_list),
            'image/object/bbox/label': int64_feature(label_list),
            'image/object/bbox/label_text': bytes_feature(type_list),
            'image/object/bbox/occlusion': int64_feature(occl_list),
            'image/object/bbox/truncation': float_feature(trun_list),
            'image/object/observation/alpha': float_feature(alpha_list),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data),
            'image/object/3Dbbox/h': float_feature(ddd_bbox_h_list),
            'image/object/3Dbbox/w': float_feature(ddd_bbox_w_list),
            'image/object/3Dbbox/l': float_feature(ddd_bbox_l_list),
            'image/object/3Dbbox/x': float_feature(ddd_bbox_x_list),
            'image/object/3Dbbox/y': float_feature(ddd_bbox_y_list),
            'image/object/3Dbbox/z': float_feature(ddd_bbox_z_list),
            'image/object/3Dbbox/ry': float_feature(ddd_bbox_ry_list)
    }))

for orgin in oriented_bbox:
        assert  len(orgin) == 8
        [l.append(point) for l, point in zip([x1, x2, x3, x4, y1, y2, y3, y4], orgin)]

    image_format = b'JPEG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/filename': bytes_feature(filename.encode('utf-8')),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/x1': float_feature(x1),
            'image/object/bbox/y1': float_feature(y1),
            'image/object/bbox/x2': float_feature(x2),
            'image/object/bbox/y2': float_feature(y2),
            'image/object/bbox/x3': float_feature(x3),
            'image/object/bbox/y3': float_feature(y3),
            'image/object/bbox/x4': float_feature(x4),
            'image/object/bbox/y4': float_feature(y4),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/object/bbox/ignored': int64_feature(ignored),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example

for b in bboxes:
        assert len(b) == 4
        # pylint: disable=expression-not-assigned
        [l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
        # pylint: enable=expression-not-assigned

    image_format = b'JPEG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example

'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(int(cam_0)),
            'cam_1': dataset_utils.int64_feature(int(cam_1)),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'attrs_w2v25_0': dataset_utils.float_feature(attrs_w2v25_0),
            'attrs_w2v25_1': dataset_utils.float_feature(attrs_w2v25_1),
            'attrs_w2v50_0': dataset_utils.float_feature(attrs_w2v50_0),
            'attrs_w2v50_1': dataset_utils.float_feature(attrs_w2v50_1),
            'attrs_w2v100_0': dataset_utils.float_feature(attrs_w2v100_0),
            'attrs_w2v100_1': dataset_utils.float_feature(attrs_w2v100_1),
            'attrs_w2v150_0': dataset_utils.float_feature(attrs_w2v150_0),
            'attrs_w2v150_1': dataset_utils.float_feature(attrs_w2v150_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_0': dataset_utils.int64_feature(pose_mask_r7_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_1': dataset_utils.int64_feature(pose_mask_r7_1.astype(np.int64).flatten().tolist()),
            'seg_0': dataset_utils.int64_feature(seg_0.astype(np.int64).flatten().tolist()),
            'seg_1': dataset_utils.int64_feature(seg_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),
            
            'indices_r4_0': dataset_utils.int64_feature(np.array(indices_r4_0).astype(np.int64).flatten().tolist()),
            'values_r4_0': dataset_utils.float_feature(np.array(values_r4_0).astype(np.float).flatten().tolist()),

'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/filename': bytes_feature(filename.encode('utf-8')),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/x1': float_feature(x1),
            'image/object/bbox/y1': float_feature(y1),
            'image/object/bbox/x2': float_feature(x2),
            'image/object/bbox/y2': float_feature(y2),
            'image/object/bbox/x3': float_feature(x3),
            'image/object/bbox/y3': float_feature(y3),
            'image/object/bbox/x4': float_feature(x4),
            'image/object/bbox/y4': float_feature(y4),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/object/bbox/ignored': int64_feature(ignored),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example

'image_name_0': dataset_utils.bytes_feature(pairs[idx][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[idx][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[idx]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(int(cam_0)),
            'cam_1': dataset_utils.int64_feature(int(cam_1)),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'attrs_w2v25_0': dataset_utils.float_feature(attrs_w2v25_0),
            'attrs_w2v25_1': dataset_utils.float_feature(attrs_w2v25_1),
            'attrs_w2v50_0': dataset_utils.float_feature(attrs_w2v50_0),
            'attrs_w2v50_1': dataset_utils.float_feature(attrs_w2v50_1),
            'attrs_w2v100_0': dataset_utils.float_feature(attrs_w2v100_0),
            'attrs_w2v100_1': dataset_utils.float_feature(attrs_w2v100_1),
            'attrs_w2v150_0': dataset_utils.float_feature(attrs_w2v150_0),
            'attrs_w2v150_1': dataset_utils.float_feature(attrs_w2v150_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_0': dataset_utils.int64_feature(pose_mask_r7_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r6_1': dataset_utils.int64_feature(pose_mask_r7_1.astype(np.int64).flatten().tolist()),
            'seg_0': dataset_utils.int64_feature(seg_0.astype(np.int64).flatten().tolist()),