%2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x2xbf16>) -> tensor<1x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  return %3 : tensor<1x2xf32>
}

// CHECK: func @cast_bf16_matmul_to_fp32
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])

  func.return %1 : tensor<16x128xf32>
  // CHECK: return %0 : tensor<16x128xf32>
}

// CHECK-LABEL: FuseTransposeFCLhsToBatchMatmul

  // MLIR @main function corresponds to the TF function "main_original".
  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir(
    func.func private @main(%arg: tensor<1x2xf32>) -> (tensor<1x2xf32>) attributes {tf._original_func_name = "main_original"} {
      return %arg : tensor<1x2xf32>
    }
  )mlir");
  ASSERT_TRUE(module_op);

  absl::flat_hash_map<FunctionName, FunctionAlias> function_aliases;

    return %1 : tensor<1x3xf32>
  }

  func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    return %0 : tensor<1x3xf32>

}

// CHECK-LABEL: @broadcast_batch_matmul_v2_rhs
func.func @broadcast_batch_matmul_v2_rhs(%arg0: tensor<17x17x17xf32>, %arg1: tensor<17x24xf32>) -> tensor<17x17x24xf32> {
  %cst = arith.constant dense<[17, 17, 24]> : tensor<3xi64>
  %0 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<17x24xf32>, tensor<3xi64>) -> tensor<17x17x24xf32>

func.func @single_state_single_dataset_type_multiple_arguments(
      %arg0: tensor<!tf_type.variant>,
      %arg1: tensor<i64>,
      %arg2: tensor<!tf_type.resource<tensor<64xf32>>>,
      %arg3: tensor<!tf_type.resource<tensor<128xf32>>>
    ) {
    // CHECK:      %[[ANON_ITER:[0-9]*]] = "tf.AnonymousIteratorV3"

func.func @tfl_wrapped_jax_random_uniform(%arg0: tensor<2xui32>) -> tuple<tensor<1x2xf32>> {
  // This is a fake jax random uniform body.
  %0 = stablehlo.constant dense<0.0> : tensor<2xf32>
  %1 = "stablehlo.reshape"(%0) : (tensor<2xf32>) -> tensor<1x2xf32>
  %2 = "stablehlo.tuple"(%1) : (tensor<1x2xf32>) -> tuple<tensor<1x2xf32>>
  func.return %2 : tuple<tensor<1x2xf32>>

  %dq_w = "tfl.dequantize"(%w) : (tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<12x2xf32>
  %emb = "tfl.gather"(%dq_w, %arg0) {axis = 0 : i32, batch_dims = 0 : i32} : (tensor<12x2xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  func.return %emb : tensor<2x2xf32>

// CHECK-NEXT: tfl.pseudo_qconst
// CHECK-NEXT: tfl.gather
// CHECK-NEXT: tfl.dequantize
}

}

// -----

// CHECK-LABEL: listPushBack
func.func @listPushBack(%arg0: tensor<!tf_type.variant<tensor<?x1xf32>>>, %arg1: tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>  {
  %0 = "tf.TensorListPushBack"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>

  %elem_2 = "tf._SecondOp"() : () -> tensor<16x1xf32>
  %tl_set_item2 = "tf.TensorListSetItem"(%tl, %one, %elem_2) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  func.return
}

// CHECK: use_two_sep_ops_empty_tensor_list