%9 = stablehlo.dot_general %8, %2, contracting_dims = [2] x [0] : (tensor<1x4x2xf32>, tensor<2x3xf32>) -> tensor<1x4x3xf32>
    %10 = stablehlo.convert %3 : (tensor<1x1x3xi32>) -> tensor<1x1x3xf32>
    %11 = stablehlo.broadcast_in_dim %10, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %12 = stablehlo.subtract %9, %11 : tensor<1x4x3xf32>  // Precalculated zp_neg.

      %%arg0 = "tf.Const"() {value = dense<-3.0> : tensor<1x4x2xf32>} : () -> tensor<1x4x2xf32>
      %%arg1 = "tf.Const"() {value = dense<-3.0> : tensor<1x2x4xf32>} : () -> tensor<1x2x4xf32>

      %%1 = "tf.%s"(%%arg0, %%arg1) {T = f32, adj_x = false, adj_y = false, grad_x = false, grad_y = false, device = ""} : (tensor<1x4x2xf32>, tensor<1x2x4xf32>) -> tensor<1x4x4xf32>

      func.return %%1 : tensor<1x4x4xf32>

  %45 = mhlo.reshape %44 : (tensor<1x64x32x1xf32>) -> tensor<1x64x32xf32>
  %46 = "mhlo.broadcast_in_dim"(%45) <{broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>}> : (tensor<1x64x32xf32>) -> tensor<1x64x32x32xf32>
  %47 = mhlo.multiply %43, %46 : tensor<1x64x32x32xf32>
  %48 = mhlo.add %30, %47 : tensor<1x64x32x32xf32>

// CHECK-LABEL: squeezeToReshape
func.func @squeezeToReshape(%arg0: tensor<1x1x2xf32>) -> tensor<2xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<1x1x2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>

  // CHECK-DAG: [[CONST:.*]] = arith.constant dense<2> : tensor<1xi32>
  // CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
  // CHECK: return %[[RESULT]]
}

  %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
}

}

// -----

func.func @testPReluInvalidBroadcast(%arg0: tensor<15x14x2x14xf32>, %arg1: tensor<1x1x3xf32>) -> tensor<15x14x2x14xf32> {
  // expected-error @+1 {{'tfl.prelu' op operands don't have broadcast-compatible shapes}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<15x14x2x14xf32>, tensor<1x1x3xf32>) -> tensor<15x14x2x14xf32>
  func.return %0 : tensor<15x14x2x14xf32>
}

// -----