# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"
  attr {

  %scales_0 = "tf.Const"() { value = dense<[1.0, 2.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %zps_0 = "tf.Const"() { value = dense<[3, 4]> : tensor<2xi32> } : () -> tensor<2xi32>
  %scales_1 = "tf.Const"() { value = dense<[3.0, 4.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %zps_1 = "tf.Const"() { value = dense<[5, 6]> : tensor<2xi32> } : () -> tensor<2xi32>

  %1 = "tf.Transpose"(%arg0, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %2 = "tf.Transpose"(%1, %0) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x8x4xf32>
  %3 = "tf.Transpose"(%arg1, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %4 = "tf.Transpose"(%3, %0) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x8x4xf32>

    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function
// CHECK-NEXT: signature
// CHECK-NEXT: name: "main"

// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
// CHECK: return %[[conv_quant]]

// CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

      %c = stablehlo.constant dense<40> : tensor<2xi32>
      %0 = "tf.UnconvertedOp"(%arg0, %c) {align_corners = false, half_pixel_centers = false} : (tensor<1x20x20x28xf32>, tensor<2xi32>) -> tensor<1x40x40x28xf32>
      func.return %0 : tensor<1x40x40x28xf32>
  }

    tf_executor.fetch
  }
  func.return

// CHECK: %[[v5:.*]] = "tf.Gather"(%3, %[[cst_1]]) <{validate_indices = true}> : (tensor<3xi32>, tensor<2xi32>) -> tensor<2xi32>
// CHECK: %[[v6:.*]] = "tf.Gather"(%4, %[[cst_2]]) <{validate_indices = true}> : (tensor<4xi32>, tensor<1xi32>) -> tensor<1xi32>
// CHECK: %[[v7:.*]] = "tf.Concat"(%[[cst_3]], %5, %6) : (tensor<i32>, tensor<2xi32>, tensor<1xi32>) -> tensor<3xi32>

  attributes {tf_saved_model.exported_names = ["assign_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.AssignVariableOp"([[name]]
    %0 = "tf.Const"() {type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32>} : () -> tensor<10xf32>
    "tf.AssignVariableOp"(%arg0, %0) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()

    // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>
    // CHECK: %[[CONCAT:.*]] = "tfl.concatenation"(%arg0, %arg1) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xf32>, tensor<1xf32>) -> tensor<2xf32>
    // CHECK: %[[VAL_1:.*]] = "tfl.reshape"(%[[CONCAT]], %[[VAL_0]]) : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
    // CHECK: return %[[VAL_1]]
  }