func.return %0 : tensor<256x9x7x16xf32>
}

// -----

// CHECK-LABEL: @conv2d_backprop_input_dynamic
func.func @conv2d_backprop_input_dynamic(%filter: tensor<2x2x1x16xf32>, %out_backprop: tensor<?x256x256x16xf32>) -> tensor<?x512x512x1xf32> {
  // CHECK: %[[REV_FILTER:.*]] = "mhlo.reverse"(%arg0) <{dimensions = dense<[0, 1]> : tensor<2xi64>}>
  // CHECK: %[[RESULT:.*]] = mhlo.convolution(%arg1, %[[REV_FILTER]])

    auto filter_ty = mlir::dyn_cast<RankedTensorType>(op.getFilter().getType());

    // With the exception of out_backprop's batch dimension, out_backprop and
    // filter need to have static shape. Filter is validated here, out_backprop
    // is mostly validated at use.
    if (!out_backprop_ty || !filter_ty || !filter_ty.hasStaticShape())
      return failure();