// CHECK: %0 = tfl.add(%arg0, %cst_0) <{fused_activation_function = "NONE"}> : (tensor<2x3x4x5xf32>, tensor<2x3x4x1xf32>) -> tensor<2x3x4x5xf32>
// CHECK: %1 = "tfl.transpose"(%0, %cst) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>

// -----

// CHECK-LABEL: pushTposeBcastScalarCstInput
func.func @pushTposeBcastScalarCstInput(%arg0: tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32> {

// -----

// CHECK-LABEL: transpose_simple_4d
func.func @transpose_simple_4d() -> tensor<5x2x3x4xf32> {
  %0 = stablehlo.constant dense<1.000000e+0> : tensor<2x3x4x5xf32>
  %1 = stablehlo.transpose %0, dims = [3, 0, 1, 2] : (tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32>
  return %1 : tensor<5x2x3x4xf32>
}
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<5x2x3x4xf32>
// CHECK-NOT: transpose

  auto build_result = BuildHloFromGraph(*graph, /*use_output_shapes=*/true);
  ASSERT_FALSE(build_result.ok());
  EXPECT_THAT(build_result.message(),
              HasSubstr("op operand type 'tensor<2x3x4x5xi32>' and result type "
                        "'tensor<1xi32>' are cast incompatible"));
}

}  // namespace