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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def dicom_to_nifti(dicom_input, output_file):
 """ This function will convert an anatomical dicom series to a nifti Examples: See unit test :param output_file: filepath to the output nifti :param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms """

    if len(dicom_input) <= 0:
        raise ConversionError('NO_DICOM_FILES_FOUND')

    # remove duplicate slices based on position and data
    dicom_input = _remove_duplicate_slices(dicom_input)

    # remove localizers based on image type
    dicom_input = _remove_localizers_by_imagetype(dicom_input)
    if settings.validate_slicecount:
        # remove_localizers based on image orientation (only valid if slicecount is validated)
        dicom_input = _remove_localizers_by_orientation(dicom_input)

        # validate all the dicom files for correct orientations
        common.validate_slicecount(dicom_input)
    if settings.validate_orientation:
        # validate that all slices have the same orientation
        common.validate_orientation(dicom_input)
    if settings.validate_orthogonal:
        # validate that we have an orthogonal image (to detect gantry tilting etc)
        common.validate_orthogonal(dicom_input)

    # sort the dicoms
    dicom_input = common.sort_dicoms(dicom_input)

    # validate slice increment inconsistent
    slice_increment_inconsistent = False
    if settings.validate_slice_increment:
        # validate that all slices have a consistent slice increment
        common.validate_slice_increment(dicom_input)
    elif common.is_slice_increment_inconsistent(dicom_input):
        slice_increment_inconsistent = True

    # if inconsistent increment and we allow resampling then do the resampling based conversion to maintain the correct geometric shape
    if slice_increment_inconsistent and settings.resample:
        nii_image, max_slice_increment = _convert_slice_incement_inconsistencies(dicom_input)
    # do the normal conversion
    else:
        # Get data; originally z,y,x, transposed to x,y,z
        data = common.get_volume_pixeldata(dicom_input)

        affine, max_slice_increment = common.create_affine(dicom_input)

        # Convert to nifti
        nii_image = nibabel.Nifti1Image(data, affine)

    # Set TR and TE if available
    if Tag(0x0018, 0x0081) in dicom_input[0] and Tag(0x0018, 0x0081) in dicom_input[0]:
        common.set_tr_te(nii_image, float(dicom_input[0].RepetitionTime), float(dicom_input[0].EchoTime))

    # Save to disk
    if output_file is not None:
        logger.info('Saving nifti to disk %s' % output_file)
        nii_image.to_filename(output_file)

    return {'NII_FILE': output_file,
            'NII': nii_image,
            'MAX_SLICE_INCREMENT': max_slice_increment}