DenseIntElementsAttr bcast_dims) {
  auto dimensions = llvm::to_vector(bcast_dims.getValues<int64_t>());
  const auto result_shape = result_type.getShape();
  // Index for the broadcasted matrix.
  llvm::SmallVector<int64_t, 16> current_index(result_type.getRank(), 0);
  // Computes the new operand shape using the original shape and the broadcast

  if (!variant_type || variant_type.getSubtypes().size() != 1) return failure();
  TensorType tensor_type = variant_type.getSubtypes().front();
  if (!tensor_type.hasStaticShape()) return failure();
  for (auto d : tensor_type.getShape()) shape->push_back(d);
  return success();
}

LogicalResult HandleEmptyTensorListOp(
    TF::EmptyTensorListOp list,
    llvm::SmallDenseMap<Value, SizeInfo>* buffer_to_size) {
  Value buffer;

    zero = builder.getFloatAttr(elem_type, 0);
  } else {
    return false;
  }
  if (auto ranked_type = dyn_cast<RankedTensorType>(type)) {
    llvm::ArrayRef<int64_t> type_shape = ranked_type.getShape();
    for (int64_t i : type_shape) {
      if (i < 0) return false;
    }
    shape = builder.getI64TensorAttr(type_shape);
  } else {
    return false;
  }
  return true;
}

GetBroadcastShapesForBatchMatmul(ShapedType input_type,
                                 ShapedType weight_type) {
  ArrayRef<int64_t> input_shape = input_type.getShape();
  ArrayRef<int64_t> weight_shape = weight_type.getShape();

  const int64_t num_matmul_dim = 2;
  const int64_t num_input_batch_dim = input_type.getRank() - num_matmul_dim;

                                          axis_op);
}

ArrayRef<int64_t> GetRankedTensorShape(Value value) {
  return mlir::cast<RankedTensorType>(value.getType()).getShape();
}

Value SliceRankedTensor(OpBuilder* builder, Value input,
                        ArrayRef<int64_t> begin_shape,
                        ArrayRef<int64_t> begin_values,

  DenseIntElementsAttr iota_const_attr;
  if (!matchPattern(iota, m_Constant(&iota_const_attr))) return false;

  auto iota_type = iota_const_attr.getType();
  auto iota_shape = iota_type.getShape();
  auto reduce_dim = (*dimensions.value_begin<APInt>()).getSExtValue();
  if (reduce_dim < 0) reduce_dim += iota_type.getRank();

  auto index =

        }

        shape_inference::ShapeHandle handle;
        TF_RETURN_IF_ERROR(
            context->MakeShapeFromPartialTensorShape(arg_shape.shape, &handle));
        TF_RETURN_IF_ERROR(shape_refiner->SetShape(n, 0, handle));
      }
    }

    // Sometimes we have VariableShape nodes in while loop (after Enter nodes).
    // They won't be constant-folded because TensorFlow constant folding does

// a TensorFlow graph.
//
// Abstractly, if we have a cluster of this form:
//
//   x0 = arg0
//   x1 = arg1
//     ...
//   shape = f(x0, x1, ...)
//   result = Reshape(input=<something>, new_shape=shape)
//
// then pulling `f` out of the cluster may reduce the number of compilations and
// will never increase the number of compilations.
//

  auto ranked_ty = mlir::dyn_cast_or_null<mlir::RankedTensorType>(ty);
  if (!ranked_ty) return {};

  int64_t rank = ranked_ty.getRank();
  llvm::SmallVector<int64_t, 4> dims = llvm::to_vector<4>(ranked_ty.getShape());
  auto encoding = mlir::dyn_cast_or_null<mlir::mhlo::TypeExtensionsAttr>(
      ranked_ty.getEncoding());
  if (encoding && !encoding.getBounds().empty()) {
    for (int64_t dim = 0; dim < rank; ++dim) {

      return op_->emitRemark()
             << "skipping legalization due to unsupported type "
             << operand.getType();
    }

    auto shape_or = expr.GetShape();
    if (!shape_or.ok()) {
      return op_->emitRemark()
             << "failed to get shape for expression. " << expr.HumanString();
    }

    tensors.emplace_back(