}

func.func @matmul_batch(%arg0: tensor<10x15xf32>, %arg1: tensor<15x17xf32>) -> tensor<10x17xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", adj_x = false, adj_y = false} :
(tensor<10x15xf32>, tensor<15x17xf32>) -> tensor<10x17xf32>
  func.return %0 : tensor<10x17xf32>
// CHECK-LABEL: matmul_batch

  // expected-error @+1 {{'tfl.prelu' op result type '10x10' not broadcast compatible with broadcasted operands's shapes '10x10x10x10'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<10x10x10x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

// -----

    }
  }
  return new_arg_type;
}

// Combination of value producer and port of value produced (e.g.,
//   <value result output>:<value in output tensor>,
// so for tf.Const -> tensor<10x20xf32>, [0,2,18] would point to a unique output
// scalar value).
struct ValuePort {
  PointerUnion<Operation*, BlockArgument> producer;
  SmallVector<unsigned int, 2> port;