docstr: Outer joins the given DataFrame with the given processed columns.
  
  NOTE: If any of the processed columns have been repartitioned, the original index is replaced with a
  monotonically increasing index, which is used to define the new divisions and align the various partitions.
docstr_tok: ["Outer","joins","the","given","DataFrame","with","the","given","processed","columns",".","NOTE",":","If","any","of","the","processed","columns","have","been","repartitioned",",","the","original","index","is","replaced","with","a","monotonically","increasing","index",",","which","is","used","to","define","the","new","divisions","and","align","the","various","partitions","."]
code: def df_like(self, df: dd.DataFrame, proc_cols: Dict[str, dd.Series]):
      """Outer joins the given DataFrame with the given processed columns.
  
      NOTE: If any of the processed columns have been repartitioned, the original index is replaced with a
      monotonically increasing index, which is used to define the new divisions and align the various partitions.
      """
      # Our goal is to preserve the index of the input dataframe but to drop
      # all its columns. Because to_frame() creates a column from the index,
      # we need to drop it immediately following creation.
      dataset = df.index.to_frame(name=TMP_COLUMN).drop(
          columns=TMP_COLUMN
      )
  
      repartitioned_cols = {}
      for k, v in proc_cols.items():
          if v.npartitions == dataset.npartitions:
              # Outer join cols with equal partitions
              v.divisions = dataset.divisions
              dataset[k] = v
          else:
              # If partitions have changed (e.g. due to conversion from Ray dataset), we handle separately
              repartitioned_cols[k] = v
  
      # Assumes that there is a globally unique index (see preprocessing.build_dataset)
      if repartitioned_cols:
          if not dataset.known_divisions:
              # Sometimes divisions are unknown despite having a usable index– set_index to know divisions
              dataset = dataset.assign(**{TMP_COLUMN: dataset.index})
              dataset = dataset.set_index(TMP_COLUMN, drop=True)
              dataset = dataset.map_partitions(
                  lambda pd_df: set_index_name(
                      pd_df, dataset.index.name
                  )
              )
  
          # Find the divisions of the column with the largest number of partitions
          proc_col_with_max_npartitions = max(
              repartitioned_cols.values(), key=lambda x: x.npartitions
          )
          new_divisions = proc_col_with_max_npartitions.divisions
  
          # Repartition all columns to have the same divisions
          dataset = dataset.repartition(new_divisions)
          repartitioned_cols = {
              k: v.repartition(new_divisions)
              for k, v in repartitioned_cols.items()
          }
  
          # Outer join the remaining columns
          for k, v in repartitioned_cols.items():
              dataset[k] = v
  
      return dataset
  
imports: import collections
  import logging
  from contextlib import contextmanager
  from typing import Dict
  import dask
  import dask.array
  import dask.dataframe
  import ray
  from dask.diagnostics import ProgressBar
  from packaging import version
  from pyarrow.fs import FSSpecHandler, PyFileSystem
  from ray.data import Dataset, read_parquet
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.data.dataframe.base import DataFrameEngine
  from ludwig.utils.data_utils import get_pa_schema, get_parquet_filename, split_by_slices
  from ludwig.utils.dataframe_utils import set_index_name
  from ludwig.utils.fs_utils import get_fs_and_path
label_desc: Use image node_id 3 for calling the DaskEngine obj's underlying member method code with example usage: obj.df_like(df, proc_cols) and returns: dataset