code: def dict_to_dataset(data, *, attrs=None, library=None, coords=None, dims=None):
      
      if dims is None:
          dims = {}
  
      data_vars = {}
      for key, values in data.items():
          data_vars[key] = numpy_to_data_array(
              values, var_name=key, coords=coords, dims=dims.get(key)
          )
      return xr.Dataset(data_vars=data_vars, attrs=make_attrs(attrs=attrs, library=library))
docs: Convert a dictionary of numpy arrays to an xarray.Dataset.
  
      Parameters
      ----------
      data : dict[str] -> ndarray
          Data to convert. Keys are variable names.
      attrs : dict
          Json serializable metadata to attach to the dataset, in addition to defaults.
      library : module
          Library used for performing inference. Will be attached to the attrs metadata.
      coords : dict[str] -> ndarray
          Coordinates for the dataset
      dims : dict[str] -> list[str]
          Dimensions of each variable. The keys are variable names, values are lists of
          coordinates.
  
      Returns
      -------
      xr.Dataset
  
      Examples
      --------
      dict_to_dataset({'x': np.random.randn(4, 100), 'y', np.random.rand(4, 100)})