%0 = arith.constant dense<0> : tensor<3xi32>
  %1 = "tfl.dynamic_update_slice"(%arg0, %arg1, %0) : (tensor<2x7x14xf32>, tensor<2x7x7xf32>, tensor<3xi32>) -> tensor<2x7x14xf32>
  // CHECK: "tfl.dynamic_update_slice"
  func.return %1 : tensor<2x7x14xf32>
}

// CHECK-LABEL: OptimizeTranposeWithRank7orMoreEffectiveRank6

  // CHECK: return %[[v3:.*]] : tensor<5x7xf32>
}

func.func @einsum_transpose_matmul(%arg0: tensor<2x5x7xf32>, %arg1: tensor<5x3x2xf32>) -> tensor<5x3x7xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "lbh,bkl->bkh"}: (tensor<2x5x7xf32>, tensor<5x3x2xf32>) -> tensor<5x3x7xf32>
  func.return %0 : tensor<5x3x7xf32>
  // CHECK-LABEL: einsum_transpose_matmul

    %15 = "quantfork.qcast"(%14) {volatile} : (tensor<2x3x3xf32>) -> tensor<2x3x3x!quant.uniform<i8:f32, 3.000000e-01:1>>
    %16 = "quantfork.dcast"(%15) : (tensor<2x3x3x!quant.uniform<i8:f32, 3.000000e-01:1>>) -> tensor<2x3x3xf32>
    return %16 : tensor<2x3x3xf32>
  }

  // CHECK: quantized_dot_general_fn_1

  module {
    func.func @main(%arg0: tensor<2x2x2xf32>, %arg1: tensor<2x2x2xf32>) -> tensor<2x2x2xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1,
        batching_dims = [0] x [0],
        contracting_dims = [2] x [1],
        precision = [DEFAULT, DEFAULT]
      : (tensor<2x2x2xf32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
        return %0 : tensor<2x2x2xf32>
    }
  }
)mlir";

  %1 = "tfl.add"(%cst_2, %cst_1) {fused_activation_function = "NONE"} : (tensor<2x2x2xf32>, tensor<  2x2xf32>) -> tensor<2x2x2xf32>
  %2 = "tfl.add"(%cst_0, %cst_2) {fused_activation_function = "NONE"} : (tensor<    2xf32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>

  func.return %0, %1, %2 : tensor<2x2xf32>, tensor<2x2x2xf32>, tensor<2x2x2xf32>

  // CHECK:  %cst = arith.constant dense<2> : tensor<1xi32>
  // CHECK:  %3 = "tfl.reduce_max"(%arg0, %cst) <{keep_dims = false}> : (tensor<2x2x4xf32>, tensor<1xi32>) -> tensor<2x2xf32>
  // CHECK:  %4 = "tfl.arg_max"(%arg0, %cst) : (tensor<2x2x4xf32>, tensor<1xi32>) -> tensor<2x2xi32>
  // CHECK:  return %3, %4 : tensor<2x2xf32>, tensor<2x2xi32>
}

// CHECK-LABEL:   func @convert_argmax_constant_non_z_axis

    %7 = stablehlo.broadcast_in_dim %6, dims = [2, 1] : (tensor<1x3xf32>) -> tensor<2x3x2xf32>
    %8 = "quantfork.qcast"(%7) {volatile} : (tensor<2x3x2xf32>) -> tensor<2x3x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    %9 = "quantfork.dcast"(%8) : (tensor<2x3x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<2x3x2xf32>
    return %9 : tensor<2x3x2xf32>
  }

  // CHECK: quantized_dot_general_fn_1