code: def create_predictions_data(
      condition_info: ConditionalInfo,
  ) -> pd.DataFrame:
      """Creates either unit level or grid level data for 'predictions' depending
      if the user passed covariates.
  
      Parameters
      ----------
      condition_info : ConditionalInfo
          Information about the conditional argument passed into the plot
          function.
  
      Returns
      -------
      pd.DataFrame
          A dataframe containing the data used to generate predictions. If no
          covariates were passed, then the original data used to fit the model
          is returned. Otherwise, a grid of values is created using the covariates
          passed into the `conditional` argument.
      """
      # unit level data uses the observed (empirical) data
      if not condition_info.covariates:
          covariates = get_model_covariates(condition_info.model)
          return condition_info.model.data[covariates]
  
      return create_grid(condition_info, None)
  
file_path: bambi/bambi/interpret/create_data.py
imports: import itertools
  from typing import Union
  from statistics import mode
  import numpy
  import pandas
  from pandas.api.types import is_categorical_dtype, is_float_dtype, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_string_dtype
  from bambi import Model
  from bambi.interpret.utils import ConditionalInfo, enforce_dtypes, get_model_covariates, VariableInfo
  from bambi.interpret.logs import log_interpret_defaults
label_desc: Use image node_id 5 for calling a global function with example usage: create_predictions_data(condition_info) and returns: create_grid, condition_info