mlir::Builder b(expected_ty.getContext());
    Tensor tensor(dtype, TensorShape({static_cast<int64_t>(values.size())}));
    tensor.flat<T>().setValues(values);

    auto value_or = ConvertTensor(tensor, &b);
    TF_ASSERT_OK(value_or.status());
    auto attr = value_or.value();

    EXPECT_EQ(attr.getShapedType().getElementType(), expected_ty);

    Tensor out;
    TF_ASSERT_OK(ConvertToTensor(attr, &out));

      converted_attrs.push_back(attr);
    } else if (attr.getName() == op.getPaddingAttrName()) {
      auto value_or = ConvertPaddingAttr(op, dnums, rewriter);
      if (failed(value_or)) {
        return failure();
      }
      attr.setValue(*value_or);
      converted_attrs.push_back(attr);
    } else if (attr.getName() == op.getWindowStridesAttrName() ||

      quant_specs.default_ranges.second.has_value()) {
    pass_manager.addNestedPass<mlir::func::FuncOp>(
        mlir::TFL::CreateDefaultQuantParamsPass(
            quant_specs.default_ranges.first.value_or(0.0),
            quant_specs.default_ranges.second.value_or(0.0),
            quant_specs.IsSignedInferenceType()));
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateQuantizePass(quant_specs));

  auto op_name = op->getName();
  auto action = target.getOpAction(op_name);
  if (!action.has_value()) {
    return "Unknown";
  }
  switch (action.value_or(ConversionTarget::LegalizationAction::Legal)) {
    case ConversionTarget::LegalizationAction::Legal:
      return "Legal";
    case ConversionTarget::LegalizationAction::Dynamic:
      return "Dynamic";

                             : nullptr;
    se::Stream* const dst_device_to_device_stream =
        should_use_substream
            ? device_to_device_master_stream->GetOrCreateSubStream().value_or(
                  nullptr)
            : dst_xla_context->GetDeviceToDeviceStream();
    TF_RET_CHECK(dst_device_to_device_stream != nullptr);
    auto return_substream = gtl::MakeCleanup(

            std::make_unique<xla::PjRtStreamExecutorBuffer>(
                device_shape, std::move(device_buffer), pjrt_client,
                pjrt_device,
                pjrt_device->default_memory_space().value_or(nullptr));
        owned_args->push_back(std::move(pjrt_buffer));
        args->push_back(owned_args->back().get());
      }
    } else {
      if (av_tensor->GetBuffer() == nullptr) {

  // Caller is responsible to call `TF_DeleteBuffer` to release the buffer.
  TF_Buffer* result = TF_NewBuffer();
  const std::string& value_str = *value;
  void* data = malloc(value_str.length());
  value_str.copy(static_cast<char*>(data), value_str.length(), 0);
  result->data = data;
  result->length = value_str.length();
  result->data_deallocator = [](void* data, size_t length) { free(data); };
  return result;
}

					header = true
				}
				pkg.valueDoc(value, printed)
				break
			}
		}
	}
}

// varsDoc prints all var documentation, if any, including a header.
// Printed is the valueDoc registry.
func (pkg *Package) varsDoc(printed map[*ast.GenDecl]bool) {
	var header bool
	for _, value := range pkg.doc.Vars {
		// Constants and variables come in groups, and valueDoc prints

      Iterator<V> valueItr = Iterators.emptyIterator();

      @Override
      public boolean hasNext() {
        return valueItr.hasNext() || valueCollectionItr.hasNext();
      }

      @Override
      public V next() {
        if (!valueItr.hasNext()) {
          valueItr = valueCollectionItr.next().iterator();
        }
        return valueItr.next();
      }
    };
  }

        K key = (K) elements[i];

        Iterator<V> valueItr = valueIterators.get(key);
        if (valueItr == null) {
          valueIterators.put(key, valueItr = sampleValuesIterator());
        }
        entries[i] = mapEntry(key, valueItr.next());
      }
      return multimapGenerator.create((Object[]) entries).keys();
    }