if (TF_GetCode(status) != TF_OK) return -1;
  auto stream = gcs_file->gcs_client.ReadObject(
      bucket, object, gcs::ReadRange(offset, offset + buffer_size));
  TF_SetStatusFromGCSStatus(stream.status(), status);
  if ((TF_GetCode(status) != TF_OK) &&
      (TF_GetCode(status) != TF_OUT_OF_RANGE)) {
    return -1;
  }
  int64_t read;
  auto content_length = stream.headers().find("content-length");

        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    async def __call__(self, request: Request) -> Optional[str]:
        authorization = request.headers.get("Authorization")
        if not authorization:
            if self.auto_error:
                raise HTTPException(
                    status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"

  return new TFE_Executor(&tensorflow::unwrap(ctx)->Executor());
}

void TFE_HostAddressSpace(TFE_Context* ctx, TF_Buffer* buf) {
  auto address_space = tensorflow::DeviceNameUtils::AddressSpace(
      tensorflow::unwrap(ctx)->HostCPUParsedName());
  auto str = tensorflow::DeviceNameUtils::ParsedNameToString(address_space);
  void* data = tensorflow::port::Malloc(str.length());

**/testData/** linguist-vendored
*Generated.java linguist-generated=true
*.kt.txt linguist-language=Kotlin
* text=auto
* eol=lf
*.png binary
*.jar binary
compiler/cli/bin/* eol=lf
compiler/cli/bin/*.bat eol=crlf
gradlew.bat eol=crlf

    int64_t op_id = op_id_it->second;
    auto op_it = op_tape->find(op_id);
    auto result_op_it = result.op_tape.find(op_id);
    if (op_id == -1 || op_it == op_tape->end() ||
        result_op_it != result.op_tape.end()) {
      continue;
    }
    CHECK(result.op_tape.emplace(op_id, op_it->second).second);
    for (auto it : op_it->second.input_tensor_id) {
      auto count_it = result.tensor_usage_counts.find(it);

  StringVectorToArrays(list, &list_ptrs, &list_lens);
  int list_total_size = 0;
  for (const auto& s : list) {
    list_total_size += s.size();
  }

  auto desc = init("list(string)");
  TF_SetAttrStringList(desc, "v", list_ptrs.get(), list_lens.get(),
                       list.size());

  auto oper = TF_FinishOperation(desc, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  TF_Tensor* analytical_tensor;
  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  int64_t num_elem_analytical = 1;
  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  auto* placeholder_t =
      TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build an abstract operation.
  auto* add_op = TF_NewAbstractOp(graph_ctx);
  TF_AbstractOpSetOpType(add_op, "Add", status.get());

        "colorMode": "value",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "auto",
        "reduceOptions": {
          "calcs": [
            "mean"
          ],
          "fields": "",
          "values": false
        },
        "showPercentChange": false,
        "text": {},
        "textMode": "auto",
        "wideLayout": true
      },
      "pluginVersion": "10.4.0",

        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    async def __call__(self, request: Request) -> Optional[str]:
        authorization = request.headers.get("Authorization")
        if not authorization:
            if self.auto_error:
                raise HTTPException(
                    status_code=HTTP_403_FORBIDDEN, detail="Not authenticated"