class_docstr: DAS beamformed phantom images and paired post-processed images.
class_docstr_tok: ["DAS","beamformed","phantom","images","and","paired","post-processed","images","."]
code: def _info(self):
      return self.dataset_info_from_configs(
          features=tfds.features.FeaturesDict(
              {
                  "das": {
                      "dB": tfds.features.Tensor(
                          shape=(None,), dtype=np.float32
                      ),
                      "real": tfds.features.Tensor(
                          shape=(None,), dtype=np.float32
                      ),
                      "imag": tfds.features.Tensor(
                          shape=(None,), dtype=np.float32
                      ),
                  },
                  "dtce": tfds.features.Tensor(
                      shape=(None,), dtype=np.float32
                  ),
                  "f0_hz": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "voltage": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "focus_cm": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "height": tfds.features.Tensor(
                      shape=(), dtype=np.uint32
                  ),
                  "width": tfds.features.Tensor(
                      shape=(), dtype=np.uint32
                  ),
                  "initial_radius": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "final_radius": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "initial_angle": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "final_angle": tfds.features.Tensor(
                      shape=(), dtype=np.float32
                  ),
                  "probe": tfds.features.Tensor(
                      shape=(), dtype=np.str_
                  ),
                  "scanner": tfds.features.Tensor(
                      shape=(), dtype=np.str_
                  ),
                  "target": tfds.features.Tensor(
                      shape=(), dtype=np.str_
                  ),
                  "timestamp_id": tfds.features.Tensor(
                      shape=(), dtype=np.uint32
                  ),
                  "harmonic": tfds.features.Tensor(
                      shape=(), dtype=np.bool_
                  ),
              }
          ),
          supervised_keys=("das/dB", "dtce"),
          homepage="https://github.com/ouwen/mimicknet",
      )
  
file_path: datasets/tensorflow_datasets/datasets/duke_ultrasound/duke_ultrasound_dataset_builder.py
imports: import csv
  import os
  from etils import epath
  import numpy
  from tensorflow_datasets.core.utils import bool_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Builder obj's underlying member method code with example usage: obj._info() and returns: self