# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \
# RUN:                  -tf-input-shapes=4:4 \
# RUN:                  -tf-input-data-types=DT_FLOAT,DT_FLOAT \
# RUN:                  -tf-output-arrays=Add \
# RUN:                  -tf-inference-type=DT_QUINT8 \
# RUN:                  -tf-input-min-values='-2,-3' \
# RUN:                  -tf-input-max-values='2,3' \
# RUN:                  --quant-stats=%s.stats \

    def "can read from stream and then restart to use another stream"() {
        def input1 = encoded("string 1")
        def input2 = encoded("string 2")

        given:
        def decoder = new KryoBackedDecoder(input1)
        decoder.readString()
        decoder.restart(input2)

        expect:
        decoder.readPosition == 0
        decoder.readString() == "string 2"

// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s

module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

// CHECK-DAG: %[[input_18:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-8.000000e-01, 1.600000e+00]> : tensor<2xf32>}> : (tensor<1x4xf32>) -> tensor<1x4xf32>
// CHECK-DAG: %[[input_19:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-2.000000e+00, 4.000000e+00]> : tensor<2xf32>}> : (tensor<1x2xf32>) -> tensor<1x2xf32>

  const llvm::MemoryBuffer* input =
      source_mgr->getMemoryBuffer(source_mgr->getMainFileID());
  std::string error;
  auto loc =
      mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0);
  std::vector<std::string> inputs;
  std::vector<std::string> outputs;
  return tflite::FlatBufferToMlir(
      absl::string_view(input->getBufferStart(), input->getBufferSize()),

  }

  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>

# 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

ABSL_DEPRECATED(
    "Please use the other overload of this function which accepts structured "
    "inputs instead of strings")
// Converts a TensorFlow GraphDef contained in `input` param into a MLIR module.
// Creates MLIR entities into the given MLIR `context`.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToMlirTranslateFunction(
    llvm::StringRef input, absl::string_view input_arrays,

                  absl::AbortedError("The input `min` must have float type."));
      OP_REQUIRES(context, context->input_type(i * 3 + 1) == DT_FLOAT,
                  absl::AbortedError("The input `max` must have float type."));
      OP_REQUIRES(
          context, context->input_type(i * 3 + 2) == DT_INT64,
          absl::AbortedError("The input `histogram` must have int64 type."));
    }
  }

      cost += GetRankedTensorSize(type);
    } else {
      // For unranked tensors, use the max size among the input tensors. This is
      // because the only dynamic information of the function should be the
      // input, so the size of dynamic tensors should be usually capped by
      // inputs' sizes.
      cost += max_arg_size_;
    }
  }

  cost_map_[op] = cost;
}

}  // namespace tfrt_compiler