def METHOD_NAME(dest_dir: str) -> None:
    log.info("health_pharma_market: start")
    #
    # Load inputs.
    #
    # Load meadow dataset.
    ds_meadow: Dataset = paths.load_dependency("health_pharma_market")
    # Read table from meadow dataset.
    tb_meadow = ds_meadow["health_pharma_market"]
    # Create a dataframe with data from the table.
    df = pd.DataFrame(tb_meadow)
    #
    # Process data.
    #
    # Harmonize country names
    log.info("health_pharma_market: harmonize_countries")
    df = geo.harmonize_countries(df=df, countries_file=paths.country_mapping_path)
    # Pivot (each variable has its own column)
    # This is done so that we can customize the metadata for each variable.
    log.info("health_pharma_market: pivot data")
    df = df.pivot(index=["country", "year"], columns=["variable", "measure"], values="value")
    # Create a new table with the processed data.
    tb_garden = Table(df, short_name=paths.short_name)
    # Add metadata to the variables in the table
    log.info("health_pharma_market: add variable metadata")
    tb_garden = add_variable_metadata(tb_garden)
    #
    # Save outputs.
    #
    # Create a new garden dataset with the same metadata as the meadow dataset.
    log.info("health_pharma_market: create dataset")
    ds_garden = create_dataset(dest_dir, tables=[tb_garden], default_metadata=ds_meadow.metadata)
    ds_garden.update_metadata(paths.metadata_path)
    # Save changes in the new garden dataset.
    ds_garden.save()
    log.info("health_pharma_market: end")