TensorShape x_shape({3, 3});
  Output x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  // Note: In Python this could just be "ii" becuase tf.einsum normalizes the
  // equation, but c++ doesn't do that.
  auto z = Einsum(scope_, {x}, "ii->");
  TensorShape z_shape({});
  RunTest({x}, {x_shape}, {z}, {z_shape});
}

TEST_F(LinalgGradTest, Einsum_TraceBroadcast) {
  TensorShape x_shape({4, 3, 3});

    builder.create<TF::AssignVariableOp>(
        restore_op.getLoc(), target_var_handle_ops[idx], restore_result);
  }
}

// TODO(b/261813194): Do not create a new RestoreV2 op when a RestoreV2 op
// already exists.
void InsertRestoreOpPass::runOnOperation() {
  ModuleOp module_op = getOperation();

  func::FuncOp session_init_func = GetInitializerFunction(

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

// This file is MACHINE GENERATED! Do not edit.

#include "tensorflow/c/experimental/ops/array_ops.h"

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tracing_utils.h"

==============================================================================*/

// This file wraps TensorFlow Graph(Def) to MLIR module conversion into passes
// to satisfy the API of MLIR pass registration. In order to do this, the
// command-line option header is pulled in.

#include <memory>
#include <utility>

#include "absl/container/flat_hash_set.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project

Status CreateCond(const Scope& scope, const CondGraphBuilderFn& cond,
                  const std::vector<Output>& inputs, Output* output) {
  // The control dependency is for constants in the cond graph, and other ops
  // that do not depend on the loop variables. This ensures that these ops are
  // in the while loop frame (since they will indirectly depend on an Enter node
  // defining the frame) and that they are executed once per loop iteration.
  //

  //   If file exists, truncate its size to 0.
  int dst_fd = open(dst, O_WRONLY | O_CREAT | O_TRUNC, open_mode);
  if (dst_fd < 0) {
    close(src_fd);
    return -1;
  }

  // Both files have been opened, do the transfer.
  // Since errno would be overridden by `close` below, save it here.
  int error_code = 0;
  if (CopyFileContents(dst_fd, src_fd, size) < 0) error_code = errno;

  close(src_fd);
  close(dst_fd);

      BlankLine();
      BlockOpen("void TestFunction()");
      {
        Statement("int i = 1");
        BlankLine();
        BlockOpen("while (i == 1)");
        {
          CommentLine("Do nothing, really....");
          CodeLine("#if 0");
          Statement("call()");
          CodeLine("#endif");
          BlockClose();
        }
        BlockClose("  // comment ending TestFunction");
      }

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

// Generated by the tf_library build rule.  DO NOT EDIT!
//
// This file contains a test and benchmark for the function generated by
// tfcompile.  All tokens of the form `{{TFCOMPILE_*}}` must be rewritten to
// real values before this file can be compiled.
//

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

// This file is MACHINE GENERATED! Do not edit.

#include "tensorflow/c/experimental/ops/io_ops.h"

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tracing_utils.h"

    rewriter.eraseOp(call_op);
    func_op.eraseResult(0);
    func_op.insertResult(0, new_call_op.getResult(0).getType(),
                         /*resultAttrs=*/nullptr);

    // Modify the quantized fused function to do dequantize+relu(6).
    rewriter.setInsertionPoint(req_op);
    Value new_result = rewriter.create<mlir::stablehlo::UniformDequantizeOp>(
        req_op.getLoc(), func_op.getResultTypes()[0], req_op.getOperand());