code: def export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() -> (
      None
  ):
      reduction = "mean"
      ignore_index = np.int64(1)
      node = onnx.helper.make_node(
          "NegativeLogLikelihoodLoss",
          inputs=["input", "target"],
          outputs=["loss"],
          reduction=reduction,
          ignore_index=ignore_index,
      )
  
      N, C, dim1, dim2 = 3, 5, 6, 6
      np.random.seed(0)
      input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
      target = np.random.randint(
          0, high=C, size=(N, dim1, dim2)
      ).astype(np.int64)
      target[0][0][0] = np.int64(1)
  
      negative_log_likelihood_loss = (
          compute_negative_log_likelihood_loss(
              input,
              target,
              reduction=reduction,
              ignore_index=ignore_index,
          )
      )
  
      expect(
          node,
          inputs=[input, target],
          outputs=[negative_log_likelihood_loss],
          name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii",
      )
  
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 9 for calling the NegativeLogLikelihoodLoss obj's underlying member method code with example usage: obj.export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() without return types