} else {
      // Remove the stale block and continue.
      RemoveFile_Locked(key.first);
    }
  }

  // Insert a new empty block, setting the bookkeeping to sentinel values
  // in order to update them as appropriate.
  auto new_entry = std::make_shared<Block>();
  lru_list_.push_front(key);
  lra_list_.push_front(key);
  new_entry->lru_iterator = lru_list_.begin();

// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),
// not the XLA's minor-to-major order.
// On-device dimensions can be padded. TFE_TensorDebugInfoOnDeviceDim returns
// the number of elements in a dimension after padding.

    "//tensorflow/tools/lib_package:jnilicenses_generate"
}

# This test ensures that all the targets built into the Python package include
# their dependencies. It's a rewritten version of the "smoke test", an older
# Python script that was very difficult to understand. See
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py
@test "Pip package includes all required //tensorflow dependencies" {

  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationToNodeDef(oper, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

// through custom device implementations).
//
// TODO(allenl): Currently these are black boxes, but we should have some way to
// inspect values. This would let people e.g. copy over most attributes and then
// modify some based on their values.

// A reference to an op's name -> attribute mapping
typedef struct TFE_OpAttrs TFE_OpAttrs;

All rights reserved.

All other contributions:

Copyright (c) 2014, the respective contributors
All rights reserved.

Caffe uses a shared copyright model: each contributor holds copyright over
their contributions to Caffe. The project versioning records all such
contribution and copyright details. If a contributor wants to further mark
their specific copyright on a particular contribution, they should indicate

  /// be accessed while holding the Block's mu lock. The data vector should only
  /// be accessed after state == FINISHED, and it should never be modified.
  ///
  /// In order to prevent deadlocks, never grab the block-cache-wide mu_ lock
  /// AFTER grabbing any block's mu lock. It is safe to grab mu without locking
  /// mu_.
  struct Block {
    /// The block data.

//
// This has three benefits:
//   * allows having newer plugins than the current core TensorFlow: the
//     additional entries in the plugin's table are just discarded;
//   * allows having older plugins than the current core TensorFlow (though
//     we are still warning users): the entries that core TensorFlow expects
//     but plugins didn't provide will be set to `nullptr` values and core

    return absl::OkStatus();
  }

  tensorflow::FullTypeDef FullType() const override {
    const FullTypeDef* ft;
    mutex_lock l(graph_->mu);
    graph_->graph.NodeType(output_.oper->node.name(), &ft);
    if (ft == nullptr) {
      return FullTypeDef();
    } else {
      return *ft;
    }
  }

  TF_Output output_;

  // For LLVM style RTTI.