code: def _find_gradient_methods(
      tape, trainable_param_indices, use_graph=True
  ):
      """Returns a dictionary with gradient information of each trainable parameter."""
  
      return {
          idx: _try_zero_grad_from_graph_or_get_grad_method(
              tape, tape.trainable_params[idx], use_graph
          )
          for idx in trainable_param_indices
      }
  
imports: from functools import partial
  import warnings
  import pennylane
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.measurements import MutualInfoMP, StateMP, VarianceMP, VnEntropyMP, ProbabilityMP
label_desc: Use image node_id 7 for calling a global function with example usage: _find_gradient_methods(tape, trainable_param_indices, use_graph) and returns: unknown