// CHECK: [[a:%.*]] = tf_mlrt.executeop([[input0]],
  // CHECK-SAME: AddV2
  // CHECK-SAME: op_key = 1
  // CHECK-NOT: __op_key
  %a = "tf.AddV2"(%input0, %const) {__op_key = 1: i32}: (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: [[b:%.*]] = tf_mlrt.executeop([[a]])
  // CHECK-SAME: Abs
  // CHECK-SAME: op_key = 2
  // CHECK-NOT: __op_key

  }
}

// -----

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: composite_and_select
  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<1x3xi1>
  // CHECK-SAME: %[[ARG3:.*]]: tensor<1x3xf32>

            }
        }
    }

    @Test
    fun `same script different target type`() {

        // given: same init, settings & build files all applying same script
        val same = withFile("same.gradle.kts", """println("Same script on ${'$'}this")""")
        val sameApply = """apply(from = "same.gradle.kts")"""
        val initScriptFile = withFile("same.init.gradle.kts", sameApply)

// CHECK-LABEL: @batchNormInference_2D_inner_features
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @batchNormInference_2D_inner_features(

    }

    def "repositories have the same id when m2 compatible and other configuration is the same"() {
        def repo = newRepo()
        def same = newRepo()
        def different = newRepo()

        given:
        repo.url = new URI("http://localhost")
        repo.patternLayout { it.m2compatible = true }
        same.url = new URI("http://localhost")
        same.patternLayout { it.m2compatible = true }

  // CHECK-LABEL: func @main
  // CHECK-SAME: %[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<f32>>> {tf.device = "/device:TPU:0"},
  // CHECK-SAME: %[[ARG1:.*]]: tensor<*x!tf_type.resource<tensor<f32>>> {tf.device = "/device:TPU:1"},
  // CHECK-SAME: %[[ARG2:.*]]: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>> {tf.device = "/device:TPU:0"},

// CHECK-SAME: proj_clip = 0.000000e+00 : f32,
// CHECK-SAME: time_major = false}> : (
// CHECK-SAME: tensor<1x2x3xf32>,
// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
// CHECK-SAME: none, none, none,
// CHECK-SAME: tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>,

// CHECK-SAME: , asymmetric_quantize_inputs = true
// CHECK: return %[[mm:.*]]

// PerTensor: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<512x12x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>,
// PerTensor: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %[[w]]) <{adj_x = false, adj_y = false
// PerTensor-SAME: , asymmetric_quantize_inputs = true
// PerTensor: return %[[mm:.*]]
}

// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]], %[[w_quant]], %[[b_quant]],
// CHECK-SAME: %[[in_scale]], %[[in_zp]], %[[w_scale]], %[[w_zp]],
// CHECK-SAME: %[[b_scale]], %[[w_zp]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_fn_0

  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG0]], [[OUTPUT]]#0)
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG1]], [[OUTPUT]]#1)
  "tf.AssignVariableOp"(%0, %2) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @read_write_packed_resource