}
	// iterate through the above set of inputs and upload the object.
	for i, input := range putObjectInputs {
		// uploading the object.
		_, err := obj.PutObject(context.Background(), input.bucketName, input.objectName, mustGetPutObjReader(t, bytes.NewReader(input.textData), input.contentLength, input.metaData[""], ""), ObjectOptions{UserDefined: input.metaData})
		// if object upload fails stop the test.
		if err != nil {

  // If shape attribute equals input operand's type's shape, fold it to input.
  std::optional<llvm::ArrayRef<int64_t>> shape_constraint = getShape();
  if (type.getShape() == shape_constraint && getInput().getType() == getType())
    return getInput();

  // If input operand's type's shape always satisfies the shape attribute, fold
  // it to input.
  if (shape_constraint.has_value() &&

    # Create and save a simple model that involves a hash table.
    inputs, outputs = self._create_and_save_vocab_table_lookup_qat_model_tf1(
        self._input_saved_model_path, tags, signature_def_key
    )

    # Make sure that the desired input key and output key is present.
    self.assertIn('input_vocabs', inputs.keys())
    self.assertIn('lookup', outputs.keys())

}

// -----

func.func @testSplitNonConstSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<i32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<i32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)
  func.return
}

func.func @testSplitUnknownRankSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<*xi32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<*xi32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)

                    def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".\${targetColor}")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    println "Input exists: \${input.exists()}"

    func.return %1 : tensor<2xi32>
  }

  //  Tests that all inputs to a function are passed and all outputs are returned for an outside compilation cluster
  //  with dynamic-shaped inputs/outputs. This test case tests that if another outside compiled returns
  //  only statically-shaped outputs, the value consumed by an outside compiled op with some dynamically shaped inputs is
  //  sent back to the host.

  return ExpandTo4DForConvImpl(a, true);
}

TypeAttr RescaleQtype(Type input, Attribute factor) {
  return quant::RescaleQuantizedType(input, factor);
}

// Returns `true` if reducing `axes` in `input` with `keep_dims=true` results in
// the specified `shape` and `false` otherwise.
static bool ShapeMatchesReduceWithKeepAxes(Value input,
                                           const mlir::Attribute &axes,

		gp2flags1flags = regInfo{inputs: []regMask{gp, gp, 0}, outputs: []regMask{gp, 0}}

		gp2flags     = regInfo{inputs: []regMask{gpsp, gpsp}}
		gp1flags     = regInfo{inputs: []regMask{gpsp}}
		gp0flagsLoad = regInfo{inputs: []regMask{gpspsbg, 0}}
		gp1flagsLoad = regInfo{inputs: []regMask{gpspsbg, gpsp, 0}}
		gp2flagsLoad = regInfo{inputs: []regMask{gpspsbg, gpsp, gpsp, 0}}
		flagsgp      = regInfo{inputs: nil, outputs: gponly}

  state.operands.reserve(num_operands + 1);
  for (int input = 0, e = num_operands + 1; input != e; ++input) {
    if (input < dst_input) {
      state.operands.push_back(dst->getOperand(input));
    } else if (input == dst_input) {
      state.operands.push_back(source->getResult(0));
    } else {
      state.operands.push_back(dst->getOperand(input - 1));
    }
  }

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