: inputs_(inputs.begin(), inputs.end()) {}

  InputList(const absl::Span<const Input>& inputs)  // NOLINT(runtime/explicit)
      : inputs_(inputs.begin(), inputs.end()) {}

  InputList(
      const std::initializer_list<Output>& out) {  // NOLINT(runtime/explicit)
    for (auto const& x : out) {
      inputs_.push_back(x);
    }
  }

    return errors::InvalidArgument(
        "Must specify a gradient input for each output.");
  }
  std::vector<bool> reachable_nodes = GetReachableNodes();
  for (const Output& input : inputs_) {
    if (!reachable_nodes[input.node()->id()]) {
      return errors::InvalidArgument(
          "Cannot compute the partial derivative for node '",
          input.node()->name(),
          "' as it's unreachable from the output node(s).");

  Init(2);
  // Create nested loop:
  //  while (input1 < 6) {
  //    inner_input1 = input1
  //    while (inner_input1 < 3) {
  //      input2 += 1
  //      inner_input1 += 2
  //    }
  //    input1 += input2
  //  }
  //
  // Expected execution with initial values input1 = input2 = 0:
  //
  // outer inner               inner_
  // step# step# input1 input2 input1
  // ------------------------------------

// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]]

  std::vector<TFE_TensorHandle*> compute_fn_inputs;
  TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f);
  TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f);
  compute_fn_inputs.push_back(input_a);
  compute_fn_inputs.push_back(input_b);

  TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(

            test {
                inputs1 = files("input1.txt")
                inputs2 = files("input2.txt", "input3.txt")
            }
        """

        when:
        succeeds "test", "--info"

        then:
        executedAndNotSkipped ':test'
        outputContains "Input property 'inputs1' file ${file("input2.txt")} has been removed."

inline SignatureDefT::SignatureDefT(const SignatureDefT &o)
      : signature_key(o.signature_key),
        subgraph_index(o.subgraph_index) {
  inputs.reserve(o.inputs.size());
  for (const auto &inputs_ : o.inputs) { inputs.emplace_back((inputs_) ? new tflite::TensorMapT(*inputs_) : nullptr); }
  outputs.reserve(o.outputs.size());

@__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>) -> (tensor<?x?xf32>, tensor<*xf32>, tensor<?x16384xf32>, tensor<16x112x112x?xf32>, tensor<16x224x224x3xf32>, tensor<*xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "inputs_0,conv1_conv2d_readvariableop_resource,conv1_biasadd_readvariableop_resource...

  state.operands.reserve(num_operands + 1);
  for (int input = 0, e = num_operands + 1; input != e; ++input) {
    if (input < dst_input) {
      state.operands.push_back(dst->getOperand(input));
    } else if (input == dst_input) {
      state.operands.push_back(source->getResult(0));
    } else {
      state.operands.push_back(dst->getOperand(input - 1));
    }
  }

  EXPECT_THAT(input2->type, Eq(TensorType_INT8));

  // Check if the quantization params of the minimum/maximum inputs match
  // after requantization
  EXPECT_THAT(input1->quantization->scale, Eq(input2->quantization->scale));
  EXPECT_THAT(input1->quantization->zero_point,
              Eq(input2->quantization->zero_point));

  // Check the input quantization params match the output ones.