// 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]]

  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.

	return results
}

// hasThreshold returns true if the threshold is in the input list
func hasThreshold(inputs []evictionapi.Threshold, item evictionapi.Threshold) bool {
	for _, input := range inputs {
		if input.GracePeriod == item.GracePeriod && input.Operator == item.Operator && input.Signal == item.Signal && compareThresholdValue(input.Value, item.Value) {
			return true
		}
	}
	return false
}

		fp31        = regInfo{inputs: []regMask{fp, fp, fp}, outputs: fponly}
		fp21clobber = regInfo{inputs: []regMask{fp, fp}, outputs: fponly}
		fpgp        = regInfo{inputs: fponly, outputs: gponly}
		gpfp        = regInfo{inputs: gponly, outputs: fponly}
		fp11        = regInfo{inputs: fponly, outputs: fponly}
		fp1flags    = regInfo{inputs: []regMask{fp}}

  // The first one will the function's output
  std::vector<TF_Output> outputs;

  // Add while loop to func_graph_
  {
    // The inputs to the while loop
    std::vector<TF_Output> inputs = {{feed1, 0}, {feed2, 0}};
    std::unique_ptr<TF_WhileParams> params(new TF_WhileParams(
        TF_NewWhile(func_graph_, &inputs[0], inputs.size(), s_)));

[[sec:task_inputs_outputs]]
== Task inputs and outputs

In the most common case, a task takes some inputs and generates some outputs.
We can consider the process of Java compilation as an example of a task.
The Java source files act as inputs of the task, while the generated class files, i.e. the result of the compilation, are the outputs of the task.

.Example task inputs and outputs
image::taskInputsOutputs.png[]

		gp21           = regInfo{inputs: []regMask{gpg, gpg}, outputs: []regMask{gp}}
		gp21nog        = regInfo{inputs: []regMask{gp, gp}, outputs: []regMask{gp}}
		gp21flags      = regInfo{inputs: []regMask{gp, gp}, outputs: []regMask{gp, 0}}
		gp2flags       = regInfo{inputs: []regMask{gpg, gpg}}
		gp2flags1      = regInfo{inputs: []regMask{gp, gp}, outputs: []regMask{gp}}

overlapping-outputs-input-comparison.png...

    Supports generating random value input tensors mapped by the `input_key`.

    Args:
      input_key: The string key that identifies the created tensor as an input.
      shape: Shape of the tensor data.
      minval: The lower bound of the generated input
      maxval: The upper bound of the generated input
      dtype: The type of the generated input - usually dtypes.float32 for float

//          automatic skipping of dummy input operations is performed.
//  ninputs - number of elements in `inputs` array
//  inputs - array of TF_Outputs that specify the inputs to the function.
//           If `ninputs` is zero (the function takes no inputs), `inputs`
//           can be null. The names used for function inputs are normalized