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."

        file("input.txt").createNewFile()
        file("input-nested.txt").createNewFile()
        file("input1.txt").createNewFile()
        file("input2.txt").createNewFile()
        file("inputs").createDir()
        file("inputs/inputA.txt").createNewFile()
        file("inputs/inputB.txt").createNewFile()
        file("inputs1").createDir()
        file("inputs2").createDir()

        buildFile """

is used instead.

Operands are replicated inputs and packed inputs.

replicated_inputs: each group of `n` inputs corresponds to an input for a single
individual replica and is mapped to a single region argument. Inside one group
the operands are matching in order the `devices` attribute. Each replicated
input must have compatible shapes and types.
packed_inputs: each input corresponds to an input broadcasted across all

// CHECK-DAG: %[[input_23:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x!quant.uniform<i16<-32767:32767>:f32, 2.736719606284107E-5>>) -> tensor<2xf32>

// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-shapes='' -tf-output-arrays=input -tf-convert-legacy-fed-inputs -o - | FileCheck --check-prefix=NODATATYPE %s

inputs-report.png...

  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
  // ------------------------------------

	// swap in and out to exercise copy-up and copy-down
	input8, output8 = output8, input8
	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)

      : 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);
    }
  }

  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(