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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 open_dataarray(filename_or_obj, group=None, decode_cf=True, mask_and_scale=None, decode_times=True, autoclose=None, concat_characters=True, decode_coords=True, engine=None, chunks=None, lock=None, cache=None, drop_variables=None, backend_kwargs=None, use_cftime=None):
 """Open an DataArray from a netCDF file containing a single data variable. This is designed to read netCDF files with only one data variable. If multiple variables are present then a ValueError is raised. Parameters filename_or_obj : str, Path, file or xarray.backends.*DataStore Strings and Paths are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF). group : str, optional Path to the netCDF4 group in the given file to open (only works for netCDF4 files). decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. mask_and_scale defaults to True except for the pseudonetcdf backend. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. decode_coords : bool, optional If True, decode the 'coordinates' attribute to identify coordinates in the resulting dataset. engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib'}, \ optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4'. chunks : int or dict, optional If chunks is provided, it used to load the new dataset into dask arrays. lock : False or duck threading.Lock, optional Resource lock to use when reading data from disk. Only relevant when using dask or another form of parallelism. By default, appropriate locks are chosen to safely read and write files with the currently active dask scheduler. cache : bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a ``pandas.Index``. drop_variables: string or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. backend_kwargs: dictionary, optional A dictionary of keyword arguments to pass on to the backend. This may be useful when backend options would improve performance or allow user control of dataset processing. use_cftime: bool, optional Only relevant if encoded dates come from a standard calendar (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. Notes ----- This is designed to be fully compatible with `DataArray.to_netcdf`. Saving using `DataArray.to_netcdf` and then loading with this function will produce an identical result. All parameters are passed directly to `xarray.open_dataset`. See that documentation for further details. See also -------- open_dataset """


    dataset = open_dataset(filename_or_obj, group=group, decode_cf=decode_cf,
                           mask_and_scale=mask_and_scale,
                           decode_times=decode_times, autoclose=autoclose,
                           concat_characters=concat_characters,
                           decode_coords=decode_coords, engine=engine,
                           chunks=chunks, lock=lock, cache=cache,
                           drop_variables=drop_variables,
                           backend_kwargs=backend_kwargs,
                           use_cftime=use_cftime)

    if len(dataset.data_vars) != 1:
        raise ValueError('Given file dataset contains more than one data '
                         'variable. Please read with xarray.open_dataset and '
                         'then select the variable you want.')
    else:
        data_array, = dataset.data_vars.values()

    data_array._file_obj = dataset._file_obj

    # Reset names if they were changed during saving
    # to ensure that we can 'roundtrip' perfectly
    if DATAARRAY_NAME in dataset.attrs:
        data_array.name = dataset.attrs[DATAARRAY_NAME]
        del dataset.attrs[DATAARRAY_NAME]

    if data_array.name == DATAARRAY_VARIABLE:
        data_array.name = None

    return data_array