Repository: Project-MONAI/MONAI
File: monai/transforms/io/dictionary.py (Python)

Code before:
        return d


class SaveImaged(MapTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.SaveImage`.

    Note:
        Image should be channel-first shape: [C,H,W,[D]].
        If the data is a patch of an image, the patch index will be appended to the filename.

    Args:
        keys: keys of the corresponding items to be transformed.
            See also: :py:class:`monai.transforms.compose.MapTransform`
        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
            For example, for data with key ``image``, the metadata by default is in ``image_meta_dict``.
            The metadata is a dictionary contains values such as ``filename``, ``original_shape``.
            This argument can be a sequence of strings, mapped to the ``keys``.
            If ``None``, will try to construct ``meta_keys`` by ``key_{meta_key_postfix}``.
        meta_key_postfix: if ``meta_keys`` is ``None``, use ``key_{meta_key_postfix}`` to retrieve the metadict.
        output_dir: output image directory.
                    Handled by ``folder_layout`` instead, if ``folder_layout`` not ``None``.
        output_postfix: a string appended to all output file names, default to ``trans``.
                        Handled by ``folder_layout`` instead, if ``folder_layout`` not ``None``.
        output_ext: output file extension name, available extensions: ``.nii.gz``, ``.nii``, ``.png``.
                    Handled by ``folder_layout`` instead, if ``folder_layout`` not ``None``.
        resample: whether to resample image (if needed) before saving the data array,
            based on the ``spatial_shape`` (and ``original_affine``) from metadata.
        mode: This option is used when ``resample=True``. Defaults to ``"nearest"``.
            Depending on the writers, the possible options are:

            - {``"bilinear"``, ``"nearest"``, ``"bicubic"``}.
              See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            - {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}.
              See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate

        padding_mode: This option is used when ``resample = True``. Defaults to ``"border"``.
            Possible options are {``"zeros"``, ``"border"``, ``"reflection"``}
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling
            [0, 255] (``uint8``) or [0, 65535] (``uint16``). Default is ``None`` (no scaling).
        dtype: data type during resampling computation. Defaults to ``np.float64`` for best precision.
            if None, use the data type of input data. To set the output data type, use ``output_dtype``.
        output_dtype: data type for saving data. Defaults to ``np.float32``.
        allow_missing_keys: don't raise exception if key is missing.
        squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel
            has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and
            then if C==1, it will be saved as (H,W,D). If D is also 1, it will be saved as (H,W). If `false`,
            image will always be saved as (H,W,D,C).
        data_root_dir: if not empty, it specifies the beginning parts of the input file's
            absolute path. It's used to compute ``input_file_rel_path``, the relative path to the file from

Review comment (suggestion):
```suggestion
            Handled by ``folder_layout`` instead, if ``folder_layout`` not ``None``.
```

Code after:
        return d


class SaveImaged(MapTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.SaveImage`.

    Note:
        Image should be channel-first shape: [C,H,W,[D]].
        If the data is a patch of an image, the patch index will be appended to the filename.

    Args:
        keys: keys of the corresponding items to be transformed.
            See also: :py:class:`monai.transforms.compose.MapTransform`
        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
            For example, for data with key ``image``, the metadata by default is in ``image_meta_dict``.
            The metadata is a dictionary contains values such as ``filename``, ``original_shape``.
            This argument can be a sequence of strings, mapped to the ``keys``.
            If ``None``, will try to construct ``meta_keys`` by ``key_{meta_key_postfix}``.
        meta_key_postfix: if ``meta_keys`` is ``None``, use ``key_{meta_key_postfix}`` to retrieve the metadict.
        output_dir: output image directory.
                    Handled by ``folder_layout`` instead, if ``folder_layout`` is not ``None``.
        output_postfix: a string appended to all output file names, default to ``trans``.
                        Handled by ``folder_layout`` instead, if ``folder_layout`` is not ``None``.
        output_ext: output file extension name, available extensions: ``.nii.gz``, ``.nii``, ``.png``.
                    Handled by ``folder_layout`` instead, if ``folder_layout`` not ``None``.
        resample: whether to resample image (if needed) before saving the data array,
            based on the ``spatial_shape`` (and ``original_affine``) from metadata.
        mode: This option is used when ``resample=True``. Defaults to ``"nearest"``.
            Depending on the writers, the possible options are:

            - {``"bilinear"``, ``"nearest"``, ``"bicubic"``}.
              See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            - {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}.
              See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate

        padding_mode: This option is used when ``resample = True``. Defaults to ``"border"``.
            Possible options are {``"zeros"``, ``"border"``, ``"reflection"``}
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling
            [0, 255] (``uint8``) or [0, 65535] (``uint16``). Default is ``None`` (no scaling).
        dtype: data type during resampling computation. Defaults to ``np.float64`` for best precision.
            if None, use the data type of input data. To set the output data type, use ``output_dtype``.
        output_dtype: data type for saving data. Defaults to ``np.float32``.
        allow_missing_keys: don't raise exception if key is missing.
        squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel
            has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and
            then if C==1, it will be saved as (H,W,D). If D is also 1, it will be saved as (H,W). If `false`,
            image will always be saved as (H,W,D,C).
        data_root_dir: if not empty, it specifies the beginning parts of the input file's
            absolute path. It's used to compute ``input_file_rel_path``, the relative path to the file from