// CHECK-LABEL: func @non_interfering_accesses
// CHECK-SAME: %[[ARG_0:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<32xf32>>>
// CHECK-SAME: %[[ARG_1:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<64xf32>>>
// CHECK-SAME: %[[ARG_2:[a-z0-9]+]]: tensor<32xf32>
// CHECK-SAME: %[[ARG_3:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<8xf32>>>
// CHECK-SAME: %[[ARG_4:[a-z0-9]+]]: tensor<*x!tf_type.resource<tensor<2xf32>>>

}

// CHECK: func @while_loop_test(%[[ARG_0:.*]]: tensor<i32>, %[[ARG_1:.*]]: tensor<i32>, %arg2: tensor<*xf32>)
func.func @while_loop_test(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<*xf32>) {
  // CHECK-NEXT: tf.WhileRegion
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):

// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}


// Test that the `is_mirrored_variable` attribute is preserved in the
// tf_device.replicate op.
// CHECK-LABEL: func @mirrored_variables

    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {

        when:
        String output1 = runBuild { launcher -> launcher.addArguments("-P$ARG_1") }

        then:
        output1.contains(ARG_1)

        when:
        String output2 = runBuild { launcher -> launcher.addArguments(["-P$ARG_1" as String]) }

        then:
        output2.contains(ARG_1)
    }

    def "Appends arguments multiple times"() {
        when:

    key: "parallel_copy"
    value {
      b: false
    }
  }
}
library {
  function {
    signature {
      name: "__inference_Dataset_map_<lambda>_8"
      input_arg {
        name: "args_0"
        type: DT_INT32
      }
      output_arg {
        name: "identity"
        type: DT_INT32
      }
    }
    node_def {
      name: "mul/y"
      op: "Const"
      attr {

# CHECK:           %[[ADD:.*]], %{{.*}} = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# CHECK:           tf_executor.fetch %[[ARG_1]], %[[ARG_0]], %[[ADD]]

# Test when non zero index output tensors are feeds, remaining ops that are
# unreachable are pruned if pruning is enabled.
#
# PRUNE-LABEL: func @main
# PRUNE-SAME:  (%[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>) -> (tensor<f32>, tensor<f32>, tensor<*xf32>)

        final ParameterizedClassDesc[] args2 = arg2.getArguments();
        assertThat(args2.length, is(1));

        final ParameterizedClassDesc arg2_1 = args2[0];
        assertThat(arg2_1.getRawClass(), is(sameClass(Integer.class)));
    }

    /**
     * @throws Exception
     */
    @Test
    public void testMethodParameterType() throws Exception {

    func.return %4 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
  func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

// CHECK:       tf.WhileRegion
// CHECK:       ^bb0
// CHECK:       tf.OpA
// CHECK:       ^bb0([[ARG_2:%[a-zA-Z0-9_]+]]
// CHECK-SAME:  [[ARG_3:%[a-zA-Z0-9_]+]]: tensor<i32>)
// CHECK-NEXT:  [[RES_3:%.*]] = "tf.AddV2"([[ARG_2]], [[RES_1]])
// CHECK-NEXT:  [[RES_4:%.*]] = "tf.Div"([[ARG_3]], [[RES_2]])
// CHECK:       "tf.Yield"([[RES_3]], [[RES_4]])