code: def _parse_var_array(self, X: np.ndarray) -> dict:
          
          arg_vars = {}
          # Get the shape and tensor rank
          shape = X.shape
          tensor_rank = len(shape)
          T = self._check_forward_mode_input_array(X)
          if tensor_rank == 0:
              #there is only 1 var name, so this will only run once
              for var_name in self.var_names:
                  arg_vars[var_name] = X
          if tensor_rank == 1 and T == shape[0]:
              #there is only 1 var name, so this will only run once
              for var_name in self.var_names:
                  arg_vars[var_name] = X.squeeze()
          elif tensor_rank == 1:
              for j, var_name in enumerate(sorted(self.var_names)):
                  arg_vars[var_name] = X[j].squeeze()
          elif tensor_rank == 2:
              for j, var_name in enumerate(sorted(self.var_names)):
                  arg_vars[var_name] = X[:,j].squeeze()
          return arg_vars
docs: Unpack the numpy array and bind each column to one of the variables in self.var_names
          Returns inferred dict of variable: val pairs