// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined
// allow us to trace the data dependencies between operations and hence compute
// gradients.
//
// `ZerosLike` is not expected to be called and returns a nullptr. The creation

            "//tensorflow/core/common_runtime/eager:placement_utils",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [
        ":abstract_tensor_handle",

    and a [python tracer](https://www.tensorflow.org/guide/profiler#events)
    which allows you to trace python function calls in your model. Usability
    improvements include better diagnostic messages and
    [profile options](https://tensorflow.org/guide/profiler#collect_performance_data)
    to customize the host and device trace verbosity level.

        "//tensorflow/core/profiler/internal:print_model_analysis",
        "//tensorflow/core/profiler/lib:traceme",
        "//tensorflow/core/profiler/rpc/client:profiler_client_impl",
        "//tensorflow/core/profiler/rpc:profiler_server_impl",
        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:direct_session",

        "Unable to find function with name: ", function_name);
    return;
  }

  tensorflow::GraphDebugInfo debug_info =
      tensorflow::StackTracesMapToGraphDebugInfo(
          function_record->stack_traces());

  string str = debug_info.SerializeAsString();
  void* data = tensorflow::port::Malloc(str.length());
  str.copy(static_cast<char*>(data), str.length(), 0);
  buf->data = data;
  buf->length = str.length();

                                      unneeded_gradients, out_gradients,
                                      absl::MakeSpan(in_gradients));
      if (!persistent_) {
        trace.backward_function_deleter(trace.backward_function);
      }
      if (!s.ok()) {
        return s;
      }
    } else {
      if (!persistent_) {
        trace.backward_function_deleter(trace.backward_function);

#include "tensorflow/core/platform/intrusive_ptr.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized
// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:

    unsigned int num_cpu_devices);

// Create a serialized tensorflow.RunOptions proto, where RunOptions.trace_level
// is set to FULL_TRACE if `enable_full_trace` is non-zero, and NO_TRACE
// otherwise.
TF_CAPI_EXPORT extern TF_Buffer* TF_CreateRunOptions(
    unsigned char enable_full_trace);

// Returns the graph content in a human-readable format, with length set in

  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {
    options.set_trace_level(tensorflow::RunOptions::FULL_TRACE);
  } else {
    options.set_trace_level(tensorflow::RunOptions::NO_TRACE);
  }
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(options, ret));
  return ret;
}

//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected: