}

  func.func @serving_default(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<*x!tf_type.string> {tf_saved_model.index_path = ["r"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "hash_table_Lookup/LookupTableFindV2:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

[[implementation_unknown]]
== Cannot use an input with an unknown implementation

This error indicates that a task uses a class as an input and Gradle cannot track the implementation of the class.
Gradle considers the implementation of the following classes as inputs to a task:

- the task class,
- the classes of the actions of the task,
- and the classes of nested inputs of the task, i.e. inputs annotated with `@Nested`.

    }

    // Populate inputs.
    // UnidirectionalSequenceRnn is expected to have 5 inputs, and none of them
    // are optional inputs.
    SmallVector<Value, 5> inputs;
    for (int i = 0; i < 5; ++i) {
      inputs.push_back(op->getOperand(i));
    }

    // Populate outputs.
    // UnidirectionalSequenceRnn should only have 1 output, and that is the
    // original ophint converted node's 2nd output.

# TODO(fengliuai): Figure out what to do here. A "golden" end-to-end test is overly fragile.

# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2

	for _, a := range c.analyzers {
		for _, inputKind := range a.Metadata().Inputs {
			if kinds.Contains(inputKind) {
				selected = append(selected, a)
				break
			}
		}
	}
	return Combine("subset", selected...)
}

// Metadata implements Analyzer
func (c *InternalCombinedAnalyzer) Metadata() Metadata {
	return Metadata{
		Name:   c.name,
		Inputs: combineInputs(c.analyzers),
	}
}

// Analyze implements Analyzer

public class ProtwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public ProtwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }

    return absl::OkStatus();
  }
};

// Computes:
//
// @tf.custom_gradient
// def f(input):
//   def grad(grads):
//     return grads[0]
//   return tf.exp(input), grad
// outputs = [f(inputs[0])]
Status ExpWithPassThroughGrad(AbstractContext* ctx,
                              absl::Span<AbstractTensorHandle* const> inputs,
                              absl::Span<AbstractTensorHandle*> outputs) {

    // expected-error @+1 {{'tf.TPUReplicatedInput' op TF2XLA TPU bridge input check: number of inputs inconsistent. num_replicas=2 no. of inputs=3}}

  if (!func || !func.isExternal()) return failure();
  // Get the inputs and attributes. The attributes include these from the
  // argument list and also these derived from the inputs.
  SmallVector<Value, 4> inputs;
  NamedAttrList argument_attrs;
  llvm::StringMap<Attribute> derived_attrs;
  if (failed(CollectInputsAndAttributes(rewriter, func, call_op, &inputs,
                                        &argument_attrs, &derived_attrs))) {