code: def count_unique_rows(
      data: pd.DataFrame, placeholder: float = -np.inf
  ) -> pd.DataFrame:
      """
      Take a dataset with repeated rows and returns another dataset,
      with unique rows and the count of rows in the original dataset
  
      Args:
          data: Input dataframe
          placeholder: Missing value placeholder
  
      Returns:
          Count dataframe
  
      Raises:
          ValueError: If the data is empty
  
      Example:
          a b c
          -----
          0 1 nan
          0 1 0
          1 0 0
          1 0 0
          1 1 1
          1 1 1
          1 1 1
  
          Is converted to :
  
          a b c    count
          ---------------
          0 1 nan  1
          0 1 0    1
          1 0 0    2
          1 1 1    3
      """
      # find a placeholder for NaNs: groupby excludes NaNs by default.
      # So we replace them with some value and later put the NaNs back
      data = data.fillna(placeholder)
      cols = list(data.columns)
  
      if "count" not in data.columns:
          data[
              "count"
          ] = 1  # Add a dummy count column to ensure that agg_data has non-empty columns
  
      agg_data = data.groupby(cols).sum().reset_index()
      agg_data.replace(placeholder, np.nan, inplace=True)
      return agg_data
  
imports: from typing import AnyStr, Dict, List, Set, Union
  import numpy
  import pandas
label_desc: Use image node_id 3 for calling a global function with example usage: count_unique_rows(data, placeholder) and returns: agg_data