Eq(TensorType_INT8));
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(1))->type(),
              Eq(TensorType_FLOAT32));
  EXPECT_THAT(subgraph->tensors[op->inputs[1]].get()->type,
              Eq(TensorType_INT8));

  // Verify FC bias should be int32 quantized.
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(2))->type(),

		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}}

      )

      inputs = {'input_vocabs': input_vocabs_placeholder}
      outputs = {
          'lookup': lookup_tensor,
          'output': output_tensor,
      }

      self._save_tf1_model(
          sess,
          output_path,
          signature_def_key,
          tags,
          inputs=inputs,
          outputs=outputs,

//  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
//           names of the operations (usually placeholders) pointed to by
//           `inputs`. These operation names should start with a letter.

    subject:
      - foo Object
    inputs:
      - other.thing String (java.lang.String) [*]

  by-type
    subject:
      - <no path> Runnable [*]
    inputs:
      - <no path> String (java.lang.String) [*]

  creator
    inputs:
      - a.b Object (a.b) [*]
'''
    }

    def "closes elements as required to bind all subjects and inputs"() {
        given:

    full_output_types.push_back(output.getType());
  }

  // Convert split sharded inputs to MANUAL sharded inputs.
  // common_split_sharding is the split sharding that is common to all inputs
  // and outputs.
  llvm::SmallVector<Value, 4> manual_inputs;
  manual_inputs.reserve(inputs.size());
  for (Value in : inputs) {
    Type shard_type;
    if (failed(GetShardShapedType(original_op, num_cores_per_replica,

----

[[config_cache:usage:invalidate]]
=== Invalidating the cache

The configuration cache is automatically invalidated when inputs to the configuration phase change.
However, certain inputs are not tracked yet, so you may have to manually invalidate the configuration cache when untracked inputs to the configuration phase change.
This can happen if you <<configuration_cache#config_cache:usage:ignore_problems,ignored problems>>.