code: def export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() -> None:
      reduction = "mean"
  
      node = onnx.helper.make_node(
          "SoftmaxCrossEntropyLoss",
          inputs=["x", "y", "w"],
          outputs=["z", "log_prob"],
          reduction=reduction,
      )
  
      N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
      np.random.seed(0)
      x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(
          np.float32
      )
      labels = np.random.randint(
          0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
      ).astype(np.int64)
      weight = np.random.rand(C).astype(np.float32)
  
      loss, log_prob = softmaxcrossentropy(
          x,
          labels,
          weight=weight,
          reduction=reduction,
          get_log_prob=True,
      )
  
      expect(
          node,
          inputs=[x, labels, weight],
          outputs=[loss, log_prob],
          name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob",
      )
  
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 26 for calling the SoftmaxCrossEntropyLoss obj's underlying member method code with example usage: obj.export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() without return types