# Buggy code
def save(
    self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None
) -> None:
    """
    Save data into a Nifti file.
    The meta_data could optionally have the following keys:

        - ``'filename_or_obj'`` -- for output file name creation, corresponding to filename or object.
        - ``'original_affine'`` -- for data orientation handling, defaulting to an identity matrix.
        - ``'affine'`` -- for data output affine, defaulting to an identity matrix.
        - ``'spatial_shape'`` -- for data output shape.

    When meta_data is specified, the saver will try to resample batch data from the space
    defined by "affine" to the space defined by "original_affine".

    If meta_data is None, use the default index (starting from 0) as the filename.

    Args:
        data: target data content that to be saved as a NIfTI format file.
            Assuming the data shape starts with a channel dimension and followed by spatial dimensions.
        meta_data: the meta data information corresponding to the data.

    See Also
        :py:meth:`monai.data.nifti_writer.write_nifti`
    """
    filename = meta_data["filename_or_obj"] if meta_data else str(self._data_index)
    self._data_index += 1
    original_affine = meta_data.get("original_affine", None) if meta_data else None
    affine = meta_data.get("affine", None) if meta_data else None
    spatial_shape = meta_data.get("spatial_shape", None) if meta_data else None

    if torch.is_tensor(data):
        data = data.detach().cpu().numpy()
    filename = create_file_basename(self.output_postfix, filename, self.output_dir)
    filename = f"{filename}{self.output_ext}"
    # change data shape to be (channel, h, w, d)
    while len(data.shape) < 4:
        data = np.expand_dims(data, -1)
    # change data to "channel last" format and write to nifti format file
    data = np.moveaxis(data, 0, -1)
    write_nifti(
        data,
        file_name=filename,
        affine=affine,
        target_affine=original_affine,
        resample=self.resample,
        output_spatial_shape=spatial_shape,
        mode=self.mode,
        padding_mode=self.padding_mode,
        dtype=self.dtype or data.dtype,
    )


# Traceback (AssertionError)
======================================================================
FAIL: test_training (__main__.IntegrationSegmentation3D)
----------------------------------------------------------------------
Traceback (most recent call last):
File "tests/test_integration_segmentation_3d.py", line 257, in test_training
rtol=1e-3,
File "/opt/conda/lib/python3.6/site-packages/numpy/testing/_private/utils.py", line 1533, in assert_allclose
verbose=verbose, header=header, equal_nan=equal_nan)
File "/opt/conda/lib/python3.6/site-packages/numpy/testing/_private/utils.py", line 846, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.001, atol=0

Mismatched elements: 6 / 6 (100%)
Max absolute difference: 0.01720129
Max relative difference: 0.04047175
x: array([0.543151, 0.471052, 0.453605, 0.438546, 0.437794, 0.407818])
y: array([0.544673, 0.475109, 0.444963, 0.427036, 0.433381, 0.42502 ])

----------------------------------------------------------------------
Ran 1 test in 92.377s

FAILED (failures=1)

# Fixed code
def save(
    self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None
) -> None:
    """
    Save data into a Nifti file.
    The meta_data could optionally have the following keys:

        - ``'filename_or_obj'`` -- for output file name creation, corresponding to filename or object.
        - ``'original_affine'`` -- for data orientation handling, defaulting to an identity matrix.
        - ``'affine'`` -- for data output affine, defaulting to an identity matrix.
        - ``'spatial_shape'`` -- for data output shape.

    When meta_data is specified, the saver will try to resample batch data from the space
    defined by "affine" to the space defined by "original_affine".

    If meta_data is None, use the default index (starting from 0) as the filename.

    Args:
        data: target data content that to be saved as a NIfTI format file.
            Assuming the data shape starts with a channel dimension and followed by spatial dimensions.
        meta_data: the meta data information corresponding to the data.

    See Also
        :py:meth:`monai.data.nifti_writer.write_nifti`
    """
    filename = meta_data["filename_or_obj"] if meta_data else str(self._data_index)
    self._data_index += 1
    original_affine = meta_data.get("original_affine", None) if meta_data else None
    affine = meta_data.get("affine", None) if meta_data else None
    spatial_shape = meta_data.get("spatial_shape", None) if meta_data else None

    if torch.is_tensor(data):
        data = data.detach().cpu().numpy()
    filename = create_file_basename(self.output_postfix, filename, self.output_dir)
    filename = f"{filename}{self.output_ext}"
    # change data shape to be (channel, h, w, d)
    while len(data.shape) < 4:
        data = np.expand_dims(data, -1)
    # change data to "channel last" format and write to nifti format file
    data = np.moveaxis(data, 0, -1)
    write_nifti(
        data,
        file_name=filename,
        affine=affine,
        target_affine=original_affine,
        resample=self.resample,
        output_spatial_shape=spatial_shape,
        mode=self.mode,
        padding_mode=self.padding_mode,
        align_corners=self.align_corners,
        dtype=self.dtype,
    )