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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 to_netcdf(self, path=None, mode='w', format=None, group=None, engine=None, encoding=None, unlimited_dims=None, compute=True):
 """Write dataset contents to a netCDF file. Parameters path : str, Path or file-like object, optional Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resulting netCDF file as bytes; in this case, we need to use scipy, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT). mode : {'w', 'a'}, optional Write ('w') or append ('a') mode. If mode='w', any existing file at this location will be overwritten. If mode='a', existing variables will be overwritten. format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). group : str, optional Path to the netCDF4 group in the given file to open (only works for format='NETCDF4'). The group(s) will be created if necessary. engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. encoding : dict, optional Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1, The `h5netcdf` engine supports both the NetCDF4-style compression encoding parameters ``{'zlib': True, 'complevel': 9}`` and the h5py ones ``{'compression': 'gzip', 'compression_opts': 9}``. This allows using any compression plugin installed in the HDF5 library, e.g. LZF. unlimited_dims : sequence of str, optional Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set via ``dataset.encoding['unlimited_dims']``. compute: boolean If true compute immediately, otherwise return a ``dask.delayed.Delayed`` object that can be computed later. """

        if encoding is None:
            encoding = {}
        from ..backends.api import to_netcdf
        return to_netcdf(self, path, mode, format=format, group=group,
                         engine=engine, encoding=encoding,
                         unlimited_dims=unlimited_dims,
                         compute=compute)