// CHECK-SAME: channel_handle = #mhlo.channel_handle
  // CHECK-SAME: handle =
  // CHECK-SAME: type = 3
  // CHECK-SAME: is_host_transfer = true
  // CHECK-SAME: mhlo.frontend_attributes = {_xla_host_transfer_handler_name = "tf_rendezvous", _xla_host_transfer_rendezvous = "recv_key_htod_0"}
  // CHECK-SAME: mhlo.sharding = "\08\01\1A\01\01\22\01\00"
  // CHECK-SAME: (!mhlo.token) -> (tensor<i32>, !mhlo.token)

// CHECK:      %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{
// CHECK-SAME:   dot_dimension_numbers = #mhlo.dot<
// CHECK-SAME:     lhs_batching_dimensions = [0],
// CHECK-SAME:     rhs_batching_dimensions = [0],
// CHECK-SAME:     lhs_contracting_dimensions = [3],
// CHECK-SAME:     rhs_contracting_dimensions = [1]
// CHECK-SAME: >}> : (tensor<3x72x1x2048xf32>, tensor<3x2048x512xf32>) -> tensor<3x72x1x512xf32>

// CHECK-SAME: batch_group_count = 1 : i64
// CHECK-SAME: dimension_numbers = "\10\03\1A\02\01\02 \02(\032\02\00\01@\03J\02\01\02"
// CHECK-SAME: explicit_padding = []
// CHECK-SAME: feature_group_count = 1 : i64
// CHECK-SAME: lhs_dilation = [1, 1]
// CHECK-SAME: padding = "VALID"
// CHECK-SAME: rhs_dilation = [2, 2]
// CHECK-SAME: rhs_quantization_axis = -1 : i64

// CHECK-SAME: f = @composite_conv2d_fn_2}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%[[PARTITIONEDCALL_0]], %[[CONST_0]])
// CHECK: %[[RELU6_0:.*]] = "tf.Relu6"(%[[BIASADD_0]])
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %[[CONST_1]])
// CHECK-SAME: f = @composite_conv2d_fn_1

// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu_fn_1}
// CHECK: %[[PARTITIONEDCALL_2:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_fn_1}

// CHECK-LABEL: func @interfering_accesses
// CHECK-SAME: %[[ARG_0:.*]]: tensor<*x!tf_type.resource<tensor<32xf32>>>
// CHECK-SAME: %[[ARG_1:.*]]: tensor<*x!tf_type.resource<tensor<64xf32>>>
// CHECK-SAME: %[[ARG_2:.*]]: tensor<32xf32>
// CHECK-SAME: %[[ARG_4:.*]]: tensor<*x!tf_type.resource<tensor<8xf32>>>
// CHECK-SAME: %[[ARG_5:.*]]: tensor<*x!tf_type.resource<tensor<2xf32>>>
// CHECK-SAME: %[[ARG_6:.*]]: tensor<2xf32>

  // CHECK: input_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  // CHECK: output_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  func.return
}

// CHECK-LABEL: func @func_without_sharding
// CHECK-SAME: (%{{[a-z0-9]+}}: tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})
// CHECK-SAME: -> (tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})

  // 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)