%1 = "tfl.broadcast_to"(%arg2, %arg3) : (tensor<?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>
  // expected-error @+1 {{'tfl.select_v2' op failed to verify that operands do not have the same shape or broadcastable shapes within the rank 5}}
  %2 = "tfl.select_v2"(%arg0, %0, %1) : (tensor<8x7x6x5x?x3x2x1xi1>, tensor<8x7x6x5x?x3x2x1xf32>, tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8x7x6x5x?x3x2x1xf32>

and broadcastable with the rightmost dimensions of `shape`.}]>:$counts,
    Arg<TensorOf<[TF_Float16, TF_Float32, TF_Float64, TF_Int32, TF_Int64]>, [{The probability of success for the binomial distribution. Must be broadcastable
with `counts` and broadcastable with the rightmost dimensions of `shape`.}]>:$probs
  );

  func.return %0 : tensor<4x2x!tf_type.stringref>
}

// -----

// TODO(hinsu): Move this to MLIR core once the test dialect have a custom type.

// Check that broadcastable trait accepts TF specific element type
// CHECK-LABEL: func @testAdd
func.func @testAdd(%arg0: tensor<4x2x!tf_type.string>, %arg1: tensor<2x!tf_type.string>) -> tensor<4x2x!tf_type.string> {

// Binary op utilities.
//===----------------------------------------------------------------------===//

// Returns whether the two values are guaranteed to be broadcastable to the
// same shape, this broadcasts size 1 tensors up to any rank. Dynamic dimensions
// must be broadcasted with a size 1 tensor or another dynamic dimension.
// Returns false on rankless.