version = 5 : i64
    } : (tensor<4x3x6x5xf32>, tensor<4x3x5x2xf32>) -> tensor<4x3x6x2xf32>
    return %0 : tensor<4x3x6x2xf32>
  }
  // CHECK: func.func private @qdq_for_dot_general_weight_per_channel_default(%[[ARG0:.+]]: tensor<4x3x6x5xf32>)
  // CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<4x3x5x2xf32>}> : () -> tensor<4x3x5x2xf32>

    func.return %result : tensor<10x24x24x64xf32>
  }

  // CHECK-LABEL: @max_pool_grad_same
  func.func @max_pool_grad_same(%orig_input: tensor<2x13x25x7xf32>, %orig_output: tensor<2x4x7x7xf32>, %grad: tensor<2x4x7x7xf32>) -> tensor<2x13x25x7xf32> {
    // CHECK: padding = dense<{{\[\[}}0, 0], [0, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>
    %result = "tf.MaxPoolGrad"(%orig_input, %orig_output, %grad) {
      data_format = "NHWC",

  func.return %rst : tensor<256x8x7x4xf32>

// LOBIT-DAG: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<4xf32>
// LOBIT-DAG: %[[CONSTANT0:.*]] = arith.constant dense<0.000000e+00> : tensor<4x3x3x3xf32>
// LOBIT: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT0]]) <{qtype = tensor<4x3x3x3x!quant.uniform<u4<1:15>:f32:0, {1.000000e+00:1,1.000000e+00:2,1.000000e+00:7,1.000000e+00:15}>>}>