%0 = "mhlo.reshape"(%arg0) : (tensor<1x1x512xf32>) -> tensor<1x512xf32>
  %1 = "mhlo.dot"(%0, %arg1) : (tensor<1x512xf32>, tensor<512x13x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x13xf32>
  %2 = "mhlo.reshape"(%1) : (tensor<1x13xf32>) -> tensor<1x1x13xf32>
  func.return %2 : tensor<1x1x13xf32>

#include "mlir/IR/Value.h"  // from @llvm-project

namespace mlir {
namespace odml {
// The following 5 different forms of mhlo::iota will be matched:
// 1. IotaOp.
// 2. IotaOp + BroadCastInDim.
// 3. IotaOp + Reshape.
// 4. Constant (folded Iota) + BroadCastInDim.
// 5. Constant (folded result).
// Moreover, the dimensions has to match the iota_dimension.
bool MatchIota(DenseIntElementsAttr dimensions, Value iota);
}  // namespace odml

  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE", tac.device = "GPU"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
  // CHECK: tac.cost = 4.040000e+01
  %1 = "tfl.reshape"(%0, %cst) {tac.device = "GPU"} : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
  func.return %1 : tensor<2x100xf32>
}

  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>
  %23 = "tf.Reshape"(%arg0, %5) {device = ""} : (tensor<1x!tf_type.string>, tensor<1xi32>) -> tensor<1x!tf_type.string>

        type, builder.getFloatAttr(element_ty, unique_index));

  if (auto qtype = mlir::dyn_cast<QuantizedType>(element_ty)) {
    mlir::RankedTensorType new_type = tensorflow::GetTypeFromTFTensorShape(
        type.getShape(), qtype.getStorageType());
    return DenseElementsAttr::get(
        new_type, builder.getIntegerAttr(qtype.getStorageType(), unique_index));
  }
  llvm_unreachable("unhandled element type");
}

  // CHECK-DAG: %[[NEW_DIMS:.*]] = arith.constant dense<[6, 1, 7, 8, 1]> : tensor<5xi32>
  // CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[NEW_DIMS]]) : (tensor<6x7x8xf32>, tensor<5xi32>) -> tensor<6x1x7x8x1xf32>

      if (v_int > 7 || v_int < -8) {
        return failure();
      }
    }

    Builder builder(op.getContext());
    auto shaped_type =
        mlir::RankedTensorType::get(const_type.getShape(), builder.getI4Type());
    auto newAttr = DenseElementsAttr::getFromRawBuffer(
        shaped_type, mlir::cast<DenseElementsAttr>(op.getValue()).getRawData());
    rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, newAttr);

  Output reshape_input =
      ops::Placeholder(root.WithOpName("reshape_input"), DT_FLOAT,
                       ops::Placeholder::Shape(TensorShape({500, 500})));
  Output reshape =
      ops::Reshape(root.WithOpName("reshape"), reshape_input, shape);

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));

  TF_ASSERT_OK(root.ToGraph(graph.get()));

  func.return %0 : tensor<1x519xf32>
}

// CHECK-LABEL:   func @reshape(
// CHECK-SAME:                  %[[VAL_0:.*]]: tensor<4x6xf32>) -> tensor<2x2x6xf32> {
// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[2, 2, 6]> : tensor<3xi64>
// CHECK:           %[[VAL_2:.*]] = "tf.Reshape"(%[[VAL_0]], %[[VAL_1]]) : (tensor<4x6xf32>, tensor<3xi64>) -> tensor<2x2x6xf32>

      %0 = mhlo.constant dense<1.000000e+00> : tensor<f64>
      %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
      %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>
      %3 = "mhlo.dynamic_iota"(%2) {iota_dimension = 0 : i64} : (tensor<1xi64>) -> tensor<?xi32>
      %4 = mhlo.multiply %3, %3 : tensor<?xi32>
      return %4 : tensor<?xi32>
    }