docstr: input_shape: [1, 3, 32]
  output_shape: [1, 3, 31]
docstr_tok: ["input_shape",":","[","1",",","3",",","32","]","output_shape",":","[","1",",","3",",","31","]"]
code: def export_lppool_1d_default() -> None:
      """input_shape: [1, 3, 32]
      output_shape: [1, 3, 31]
      """
      p = 3
      kernel_shape = [2]
      strides = [1]
      node = onnx.helper.make_node(
          "LpPool",
          inputs=["x"],
          outputs=["y"],
          kernel_shape=kernel_shape,
          strides=strides,
          p=p,
      )
      x = np.random.randn(1, 3, 32).astype(np.float32)
      x_shape = np.shape(x)
      pads = None
      out_shape, _ = get_output_shape_explicit_padding(
          pads, x_shape[2:], kernel_shape, strides
      )
      padded = x
      y = pool(
          padded,
          x_shape,
          kernel_shape,
          strides,
          out_shape,
          "LPPOOL",
          p=p,
      )
  
      expect(
          node, inputs=[x], outputs=[y], name="test_lppool_1d_default"
      )
  
file_path: onnx/onnx/backend/test/case/node/lppool.py
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
  from onnx.reference.ops.op_pool_common import get_output_shape_auto_pad, get_output_shape_explicit_padding, get_pad_shape, pool
label_desc: Use image node_id 1 for calling the LpPool obj's underlying member method code with example usage: obj.export_lppool_1d_default() without return types