package org.gradle.internal.declarativedsl.analysis

import org.gradle.declarative.dsl.schema.DataClass
import org.gradle.declarative.dsl.schema.DataProperty
import org.gradle.declarative.dsl.schema.DataType
import org.gradle.declarative.dsl.schema.FqName
import org.gradle.internal.declarativedsl.language.AccessChain
import org.gradle.internal.declarativedsl.language.LanguageTreeElement
import org.gradle.internal.declarativedsl.language.LocalValue

void InsertDummyIslandForFetch(FetchOp fetch) {
  llvm::SmallVector<Value, 4> data_fetches;
  llvm::SmallVector<Type, 4> data_types;
  llvm::SmallVector<Value, 4> control_fetches;
  data_fetches.reserve(fetch.getFetches().size());
  data_types.reserve(data_fetches.capacity());
  control_fetches.reserve(data_fetches.capacity());

  for (auto value : fetch.getFetches()) {

//
// =============================================================================

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;

package org.gradle.internal.declarativedsl.demo

import org.gradle.declarative.dsl.schema.AnalysisSchema
import org.gradle.declarative.dsl.schema.DataType
import org.gradle.internal.declarativedsl.analysis.OperationId
import org.gradle.internal.declarativedsl.analysis.ResolutionResult
import org.gradle.internal.declarativedsl.analysis.Resolver
import org.gradle.internal.declarativedsl.analysis.ref
import org.gradle.internal.declarativedsl.analysis.tracingCodeResolver

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.gradle.declarative.dsl.schema


interface DataClass : DataType {
    val name: FqName
    val supertypes: Set<FqName>
    val properties: List<DataProperty>
    val memberFunctions: List<SchemaMemberFunction>
    val constructors: List<DataConstructor>

    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method

namespace tensorflow {

void XlaHostRecvDeviceContext::CopyDeviceTensorToCPU(
    const Tensor* device_tensor, StringPiece tensor_name, Device* device,
    Tensor* cpu_tensor, StatusCallback done) {
  DataType dtype = EncodePrimitiveTypeAsDataType(shape_.element_type()).value();
  TensorShape tensor_shape;
  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

  // DT_FLOAT, DT_HALF, DT_QINT8, and DT_QUINT8. When DT_HALF is used, the
  // `weight_quantization` flag needs to set to true. When DT_QUINT8 is used,
  // the `weight_quantization` flag needs to set to false.
  tensorflow::DataType inference_type = tensorflow::DT_FLOAT;

  // The input and output data type during inference. This flag is only used
  // when `inference_type` is different from DT_FLOAT. This flag can only be set

  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var1", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var2", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
}

Status FakeSession::Create(const tensorflow::GraphDef& graph) {
  return tensorflow::errors::Unimplemented("not available");
}

  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()), ", device=\"",
                      device_name, "\")");
}

Status ImmediateExecutionTensorHandle::SummarizeValue(
    std::string& summary) const {
  Status status;
  AbstractTensorPtr resolved(