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

    ],
    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",

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.

build:nvcc_clang --@local_config_cuda//:cuda_compiler=nvcc


# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:

//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

    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",

//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//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

    *   tf.data input pipelines can now be executed in debug mode, which
        disables any asynchrony, parallelism, or non-determinism and forces
        Python execution (as opposed to trace-compiled graph execution) of
        user-defined functions passed into transformations such as `map`. The
        debug mode can be enabled through
        `tf.data.experimental.enable_debug_mode()`.
*   `tf.lite`