module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";

  CreateModule(kMlirModuleStr);
  auto result = Run();

  EXPECT_TRUE(result.succeeded());
}

    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"

    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
versions {
  producer: 216
}

# NONE: func @main(%arg0: tensor<i1>, %arg1: tensor<1xi32>) -> (tensor<i1>, tensor<1xi32>)

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      return %0 : tensor<1xi32>
    }
  })";

  CellReader<int64_t> dynamism_op_counter(kDynamismOpCounterStreamzName);
  CellReader<int64_t> dynamism_function_counter(

      %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>
  }

         padding = "EXPLICIT",
         strides = [5, 6, 7, 8]
       } : (tensor<1x32x32x3xf32>, tensor<4xi32>, tensor<1x32x32x8xf32>)
         -> tensor<1x1x3x8xf32>

  func.return %0 : tensor<1x1x3x8xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput
func.func @transposeConv2DBackpropInput(
  %input_sizes: tensor<4xi32>,
  %filter: tensor<1x1x3x8xf32>,
  %out_backprop: tensor<1x32x32x8xf32>

         padding = "VALID",
         strides = [1, 1, 1, 1]
       } : (tensor<1x28x28x64xf16>, tensor<4xi32>, tensor<1x28x28x64xf16>)
        -> tensor<1x1x64x64xf16>

  func.return %0 : tensor<1x1x64x64xf16>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput_f16
func.func @transposeConv2DBackpropInput_f16(
  %input_size:   tensor<4xi32>,
  %filter:       tensor<1x28x28x64xf16>,
  %out_backprop: tensor<1x28x28x64xf16>

    tf_executor.fetch
  }
  func.return

  %0 = "tf.Const"() {value = dense<["foo", "bar"]> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>
  %1 = "tf.Const"() {value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>
  %2:2 = "tf.RestoreV2"(%arg0, %0, %1) : (tensor<!tf_type.string>, tensor<2x!tf_type.string>, tensor<2x!tf_type.string>) -> (tensor<*xf32>, tensor<*xi32>)

func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}

}

// CHECK-LABEL: @tfInplaceUpdate