code: def _conversions_defined(scitype: str):
      """Return an indicator matrix which conversions are defined for scitype.
  
      Parameters
      ----------
      scitype: str - name of scitype for which conversions are queried
          valid scitype strings, with explanation, are in datatypes.SCITYPE_REGISTER
  
      Returns
      -------
      conv_df: pd.DataFrame, columns and index is list of mtypes for scitype
              entry of row i, col j is 1 if conversion from i to j is defined,
                                       0 if conversion from i to j is not defined
      """
      pairs = [
          (x[0], x[1])
          for x in list(convert_dict.keys())
          if x[2] == scitype
      ]
      cols0 = {
          x[0] for x in list(convert_dict.keys()) if x[2] == scitype
      }
      cols1 = {
          x[1] for x in list(convert_dict.keys()) if x[2] == scitype
      }
      cols = sorted(list(cols0.union(cols1)))
  
      mat = np.zeros((len(cols), len(cols)), dtype=int)
      nkeys = len(cols)
      for i in range(nkeys):
          for j in range(nkeys):
              if (cols[i], cols[j]) in pairs:
                  mat[i, j] = 1
  
      conv_df = pd.DataFrame(mat, index=cols, columns=cols)
  
      return conv_df
  
file_path: sktime/sktime/datatypes/_convert.py
imports: from copy import deepcopy
  import numpy
  import pandas
  from sktime.datatypes._check import mtype
  from sktime.datatypes._hierarchical import convert_dict_Hierarchical
  from sktime.datatypes._panel import convert_dict_Panel
  from sktime.datatypes._proba import convert_dict_Proba
  from sktime.datatypes._registry import mtype_to_scitype
  from sktime.datatypes._series import convert_dict_Series
  from sktime.datatypes._table import convert_dict_Table
label_desc: Use image node_id 4 for calling a global function with example usage: _conversions_defined(scitype) and returns: conv_df