input: "z:1"
  input: "z:2"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
versions {
  producer: 230
}

# Test non zero index output tensors as feeds. Original ops where their outputs
# are replaced with feeds are preserved and args and rets are lifted to the
# function. Rets that happen to coincide with a feed should have its value be
# of the feed.
#

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"

Jacques Pienaar <******@****.***> 1587687343 -0700

     */
    public ImmutableList<String> getExecutionReasons() {
        return executionReasons;
    }

    /**
     * If a previously produced output was reused in some way, the reused output's origin metadata is returned.
     */
    public Optional<OriginMetadata> getReusedOutputOriginMetadata() {
        return Optional.ofNullable(reusedOutputOriginMetadata);
    }

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

  // CHECK-DAG: %[[OUTPUT_1:.*]] = "tf.Cast"(%[[OUTPUT_QINT]]) <{Truncate = false}> : (tensor<1x2x2x1x!tf_type.qint32>) -> tensor<1x2x2x1xi32>
  %output = "tf.Cast"(%q_output) {Truncate = false} : (tensor<1x2x2x1x!tf_type.qint32>) -> tensor<1x2x2x1xi32>

Type 'Writer' property 'output' is not writable because '${location}' is not a directory.

Reason: Expected '${location}' to be a directory but it's a file.

Possible solution: Make sure that the 'output' is configured to a directory.

${validationMessage("cannot_write_output")}
"""

        when:
        render cannotWriteToDir {
            type('Writer').property('output')
            dir(location)

}

// CHECK-LABEL: QuantizeWithoutNorm
func.func @QuantizeWithoutNorm(%arg0: tensor<1x1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
  %none = "tfl.no_value"() {value = unit} : () -> none
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x1x5xf32>) -> tensor<1x1x5xf32>

  const TF_TensorSpec* tensor_spec_out =
      TF_SignatureDefParamTensorSpec(param_out);
  const TF_Shape* shape_out = TF_TensorSpecShape(tensor_spec_out);

  // Output "output_0" is a scalar, float32 tensor
  EXPECT_EQ("output_0", std::string(TF_SignatureDefParamName(param_out)));
  EXPECT_EQ(TF_FLOAT, TF_TensorSpecDataType(tensor_spec_out));
  EXPECT_EQ(0, TF_ShapeDims(shape_out));

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_ce3dfbfc-7367-4d62-9d48-d13bf8125391", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.LookupTableSizeV2"(%outputs) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>