<root>
                <mxCell id="0"/>
                <mxCell id="1" parent="0"/>
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                    <mxGeometry relative="1" as="geometry">
                        <Array as="points">

                         size_t length) {
  auto s = tensorflow::unwrap(op)->SetAttrString(
      attr_name, static_cast<const char*>(value), length);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {

        // TODO Auto-generated method stub
        return null;
    }

    @Override
    public String generateType3Msg(final String arg0, final String arg1, final String arg2, final String arg3, final String arg4)
            throws NTLMEngineException {
        // TODO Auto-generated method stub
        return null;
    }

  TFE_OpSetAttrShape(handle_op.get(), "shape", /*dims=*/nullptr, /*num_dims=*/0,
                     status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  auto outputs =
      parallel_device.Execute(context.get(), std::vector<ParallelTensor*>(),
                              "VarHandleOp", TFE_OpGetAttrs(handle_op.get()),

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                </mxCell>
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                    <mxGeometry relative="1" as="geometry">
                        <Array as="points">

5,\n.my-n5 {\n  margin-bottom: -3rem !important;\n}\n\n.ml-n5,\n.mx-n5 {\n  margin-left: -3rem !important;\n}\n\n.m-auto {\n  margin: auto !important;\n}\n\n.mt-auto,\n.my-auto {\n  margin-top: auto !important;\n}\n\n.mr-auto,\n.mx-auto {\n  margin-right: auto !important;\n}\n\n.mb-auto,\n.my-auto {\n  margin-bottom: auto !important;\n}\n\n.ml-auto,\n.mx-auto {\n  margin-left: auto !important;\n}\n\n@media (min-width: 576px) {\n  .m-sm-0 {\n    margin: 0 !important;\n  }\n  .mt-sm-0,\n  .my-sm-0...

  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }
  for (int i = 0; i < *num_outputs; ++i) {
    auto logged_tensor = std::make_unique<LoggedTensor>(unwrapped_outputs[i]);
    outputs[i] = MakeLoggedTensorHandle(context, dev->device_name,

      "options": {
        "colorMode": "none",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "horizontal",
        "reduceOptions": {
          "calcs": [
            "lastNotNull"
          ],
          "fields": "",
          "values": false
        },
        "textMode": "auto"
      },
      "pluginVersion": "10.1.5",
      "targets": [
        {

using tensorflow::gtl::ArraySlice;

namespace tensorflow {
namespace tracing {
namespace graph {

class GraphContext;
class GraphOperation;
class GraphTensor;

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.

                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {