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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 merge(objects, compat='no_conflicts', join='outer'):
 """Merge any number of xarray objects into a single Dataset as variables. Parameters objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]] Merge together all variables from these objects. If any of them are DataArray objects, they must have a name. compat : {'identical', 'equals', 'broadcast_equals', 'no_conflicts'}, optional String indicating how to compare variables of the same name for potential conflicts: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. join : {'outer', 'inner', 'left', 'right', 'exact'}, optional How to combine objects with different indexes. Returns ------- Dataset Dataset with combined variables from each object. Examples -------- <xarray.Dataset> Dimensions: () Coordinates: *empty* Data variables: var0 int64 0 var1 int64 1 var2 int64 2 var3 int64 3 var4 int64 4 Raises ------ xarray.MergeError If any variables with the same name have conflicting values. See also -------- concat """
  # noqa
    from .dataarray import DataArray
    from .dataset import Dataset

    dict_like_objects = [
        obj.to_dataset() if isinstance(obj, DataArray) else obj
        for obj in objects]

    variables, coord_names, dims = merge_core(dict_like_objects, compat, join)
    # TODO: don't always recompute indexes
    merged = Dataset._construct_direct(
        variables, coord_names, dims, indexes=None)

    return merged