"new File(\"${filePath}\").$checkKind()"
        }
    }

    static FileCheck fileExists(String filePath) {
        new FileCheck(filePath, "exists")
    }

    static FileCheck fileIsFile(String filePath) {
        new FileCheck(filePath, "isFile")
    }

    static FileCheck fileIsDirectory(String filePath) {
        new FileCheck(filePath, "isDirectory")
    }

// RUN: tf-to-stablehlo-translate %s --input-arg-shapes=1 -o - | FileCheck %s

// CHECK-LABEL: func.func @main
// CHECK: %[[UQ:.*]] = stablehlo.uniform_quantize %arg0 : (tensor<1xf32>) -> tensor<1x!quant.uniform<i8:f32, 1.000000e+00:3>>
// CHECK: %[[BITCAST_CONVERT_0:.*]] = stablehlo.bitcast_convert %[[UQ]] : (tensor<1x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<1xi8>

// RUN: tf-tfrt-opt -tf-executor-to-tfrt-pipeline=decompose-resource-ops=true %s | FileCheck %s --dump-input=fail

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 293 : i32}} {

// CHECK-LABEL: func @gather
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg0:%.*]]: !tfrt_fallback.tf_tensor, [[arg1:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK: [[const:%.*]] = tfrt_fallback_async.const_dense_tensor

// RUN: tf-tfrt-opt -split-input-file -tf-mlrt-rewrite-ifrt-load-variable %s | FileCheck %s

// Variable is used by both CPU and TPU
//
// CHECK-LABEL: func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    [[HANDLE:%.*]] = "tf.VarHandleOp"()
// CHECK-NEXT:    [[ARRAYKEY:%.*]], [[FURTURE:%.*]] = "tf_mlrt.tf_ifrt_load_variable"([[HANDLE]])

// RUN: tf-opt %s -split-input-file -tf-xla-broadcast | FileCheck %s

// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \
// RUN:   | FileCheck %s

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \
// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_to_string - \
// RUN:   | FileCheck --check-prefix=FB %s

// Tests for verifying the tflite model with min/max can be imported
// correctly.

// NOLINTNEXTLINE

// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail

// CHECK-LABEL: func @string_tensor
func.func @string_tensor() -> (tensor<0x!tf_type.string>, tensor<7x!tf_type.string>) {
  // CHECK: {shape = [0], value = []}
  %0 = "tf.Const"() {value = dense<[]> : tensor<0x!tf_type.string>} : () -> tensor<0x!tf_type.string>

// RUN: odml-converter --shlo-simplify %s -split-input-file | FileCheck %s

func.func @foldDiv() -> tensor<2xf32> {
  %0 = stablehlo.constant dense<[2.0, 3.0]> : tensor<2xf32>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf32>
  %2 = stablehlo.divide %0, %1 : tensor<2xf32>
  return %2 : tensor<2xf32>
}

// CHECK-LABEL: foldDiv
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf32>

// -----

config.test_source_root = config.mlir_test_dir

# test_exec_root: The root path where tests should be run.
config.test_exec_root = os.environ['RUNFILES_DIR']

if platform.system() == 'Windows':
  tool_patterns = [
      ToolSubst('FileCheck.exe', unresolved='fatal'),
      #  Handle these specially as they are strings searched for during testing.
      ToolSubst('count.exe', unresolved='fatal'),
      ToolSubst('not.exe', unresolved='fatal')
  ]

// RUN: tf-opt %s --tf-decompose-optionals --split-input-file | FileCheck %s

// CHECK-LABEL: @from_value
func.func @from_value(%arg0: tensor<f32>) {
  // CHECK-NOT: Optional
  %0 = "tf.OptionalFromValue"(%arg0) : (tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  return
}

// -----

// CHECK-LABEL: @get_value
func.func @get_value(%arg0: tensor<!tf_type.variant<tensor<f32>>>) {
  // CHECK-NOT: Optional