// CHECK: return %[[cast_i32]] : !tfr.tensor
}

// CHECK-LABEL: decompose_output_type
func.func @decompose_output_type(%arg0: tensor<2xf32>) -> tensor<2xi32> {
  %0 = "tf.CastFloat"(%arg0) : (tensor<2xf32>) -> tensor<2xi32>
  func.return %0: tensor<2xi32>
// CHECK: %[[i32:.*]] = tfr.constant i32 -> !tfr.attr
// CHECK: tfr.call @tf__cast(%[[casted_arg:.*]], %[[i32]], %false) : (!tfr.tensor, !tfr.attr, i1) -> !tfr.tensor

// QDQ-NEXT:  return %[[split]]#0 : tensor<2xf32>
}

// CHECK-LABEL: FoldTranspose
func.func @FoldTranspose(%arg0: tensor<1x10x20x3xf32>) -> tensor<1x20x40x16xf32> {
  %cst = arith.constant dense<[1, 20, 40, 16]> : tensor<4xi32>
  %cst_0 = arith.constant dense<[2, 0, 1, 3]> : tensor<4xi32>

// CHECK-NEXT:  buffer: 10
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<[1, 401408]> : tensor<2xi32>} : () -> tensor<2xi32> loc("Const")
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

// CHECK-DAG: %[[START:.+]] = arith.constant dense<{{\[1, 2\]}}> : tensor<2xi32>
// CHECK-DAG: %[[SIZE:.+]] = arith.constant dense<2> : tensor<2xi32>
// CHECK: %[[SLICE:.+]] = "tfl.slice"(%[[ARG0]], %[[START]], %[[SIZE]]) : (tensor<3x4x!quant.uniform<i8:f32, 2.000000e+00:-1>>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x2x!quant.uniform<i8:f32, 2.000000e+00:-1>>
// CHECK: return %[[SLICE]]

// -----

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module {
      "tf_saved_model.global_tensor"() {type = tensor<2xi32>, value = dense<2> : tensor<2xi32>, sym_name = "__tf_saved_model_x"} : () -> ()
      func.func @main(%arg_0: tensor<!tf_type.resource<tensor<2xi32>>> {tf_saved_model.bound_input = @__tf_saved_model_x}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }

// -----

// CHECK-LABEL: func_with_tfl_attrs
func.func @func_with_tfl_attrs(%arg0: tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> attributes {tf.entry_function = {inputs = "arg0,arg1", outputs = "arg0"}} {
  return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

// -----

// There are cases where ODML converter relies on constants not being folded or

// CHECK:   %{{.*}} = "tfl.dequantize"(%{{.*}}) : (tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>) -> tensor<1x401408xf32>

  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>

}

// CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
// CHECK:  [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-SAME: }
# CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{{{.*dense<.*> : tensor<2xi32>.*}}}> {{{.*}}} : () -> tensor<2xi32>
# CHECK: %[[RAND_STD_NORMAL:.*]] = "tf.RandomStandardNormal"(%[[CST_0]])
# CHECK: "tf.AssignVariableOp"(%[[ARG_0]], %[[RAND_STD_NORMAL]]){{.*}}: (tensor<!tf_type.resource<tensor<1x3xf32>>>, tensor<1x3xf32>) -> ()

  // CHECK-NEXT:   s: " 0\n\000\000"
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_INT32", value = "\200\n\00\00", listvalue = ["\20\0A"]} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return