TF::ReshapeOp GetReshapeOpForConv2DFilter(ArrayRef<int64_t> new_shape,
                                          Value input, OpBuilder* builder) {
  auto reshape_result_type =
      RankedTensorType::get(new_shape, getElementTypeOrSelf(input));
  auto reshape_type = RankedTensorType::get(
      {static_cast<int64_t>(new_shape.size())}, builder->getIntegerType(64));

    // Get the new shape.
    SmallVector<int64_t, 4> new_shape;
    for (int i = 0; i < 4 - rank; ++i) {
      new_shape.push_back(1);
    }
    for (auto size : input_type.getShape()) {
      new_shape.push_back(size);
    }

    auto reshape_op =
        InsertReshapeOp(concat_op.getLoc(), input, input_type.getElementType(),
                        new_shape, &rewriter);
    reshape_ops.push_back(reshape_op.getResult());

    "sync_bg": "not set",
}

errors = []


async def get_state():
    return state


class AsyncDependencyError(Exception):
    pass


class SyncDependencyError(Exception):
    pass


class OtherDependencyError(Exception):
    pass


async def asyncgen_state(state: Dict[str, str] = Depends(get_state)):
    state["/async"] = "asyncgen started"
    yield state["/async"]

            }
        }

        lock.lock();
        try {
            setState(newState);
            this.execResult = newResult;
        } finally {
            lock.unlock();
        }

        LOGGER.debug("Process '{}' finished with exit value {} (state: {})", displayName, exitValue, newState);
    }

    @Nullable

  }
  int rank = shaped_type.getRank();
  if (rank < 2) {
    return nullptr;
  }
  SmallVector<int64_t> new_shape(shaped_type.getShape().begin(),
                                 shaped_type.getShape().end());
  std::swap(new_shape[rank - 1], new_shape[rank - 2]);
  return shaped_type.clone(new_shape);
}

// Returns a ShapedType for a permutation and the shape of input after

                    result.collectFilesInto(includedFiles, new HashSet<File>());
                    SourceFileState newState = new SourceFileState(fileContent, result.result == IncludeFileResolutionResult.UnresolvedMacroIncludes, ImmutableSet.copyOf(includedFiles));
                    current.setState(sourceFile, newState);
                    if (newState.isHasUnresolved()) {
                        hasUnresolvedHeaders = true;
                    }

          slice_sizes));

  // Collapses dimensions by reshaping.
  SmallVector<int64_t> new_shape(operand_rank - collapsed_dims.size());
  for (int64_t i = 0, j = 0; i < operand_rank; i++) {
    if (!collapsed_dims.contains(i)) {
      new_shape[j++] = operand_shape[i];
    }
  }
  if (!new_shape.empty()) new_shape[0] = -1;
  return builder.create<TF::ReshapeOp>(
      loc, output.getType(), slice_op,

            return fromState.hasSeenStateIgnoringTransitions(toState);
        }

        @Override
        public CurrentState<T> nextState(T toState) {
            return fromState.nextState(toState);
        }
    }

    /**
     * A previous operation has failed.
     */
    private static class Failed<T> extends CurrentState<T> {

    TensorType new_shape = RankedTensorType::get(
        {cur_shape[0], cur_shape[1], 1, cur_shape[2] * cur_shape[3]},
        attr.getElementType());

    // Inserts a reshape op.
    auto shape_spec_type =
        RankedTensorType::get({cur_rank}, rewriter.getIntegerType(64));
    auto new_shape_const_attr =
        DenseElementsAttr::get(shape_spec_type, new_shape.getShape());

        var reportFile: File?
        modifyState {
            val (newState, outputFile) = commitReportTo(outputDirectory, buildDisplayName, cacheAction, requestedTasks, totalProblemCount)
            reportFile = outputFile
            newState
        }
        return reportFile
    }

    private
    inline fun modifyState(f: State.() -> State) {