// When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.
  if (use_tfrt) {
    TF_Buffer* buf = TF_NewBuffer();
    TFE_GetExecutedOpNames(ctx, buf, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

Austin Anderson <******@****.***> 1698867487 -0700

//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_ops_2
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell

debugging information with `--config=dbg`, as issues occur on Linux if
there is too much debug info (see [this GitHub
issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If
you want to debug a kernel, you can compile specific files with `-g` using the
`--per_file_copt` bazel option. For example, if you want to debug the Identity
op, which are in files starting with `identity_op`, you can run

```bash

          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();
      if (flags::Global().more_stack_traces.value()) {
        *graph_def.mutable_debug_info() = graph.BuildDebugInfo();
      }
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));
      if (!status->status.ok()) {

                                                     TF_Status* status);

// Get a comma-separated list of op names executed in graph functions dispatched
// to `ctx`. This feature is currently only enabled for TFRT debug builds, for
// performance and simplicity reasons.
TF_CAPI_EXPORT extern void TFE_GetExecutedOpNames(TFE_Context* ctx,
                                                  TF_Buffer* buf,

    ],
    deps = [
        ":immediate_execution_operation",
        "//tensorflow/c:conversion_macros",
    ],
)

cc_library(
    name = "tfe_tensor_debug_info_internal",
    hdrs = ["tfe_tensor_debug_info_internal.h"],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/core:lib",
    ],
)

cc_library(
    name = "tfe_tensorhandle_internal",

        "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;

Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

    visibility = ["//visibility:public"],
)

config_setting(
    name = "linux_riscv64",
    values = {"cpu": "riscv64"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "debug",
    values = {
        "compilation_mode": "dbg",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "optimized",
    values = {
        "compilation_mode": "opt",