if (!mainIdSet.contains(docs.get(i).get(idField))) {
                offset++;
            }
        }
        if (logger.isDebugEnabled()) {
            logger.debug("The offset is {} and the fused docs is {}.", offset, docs.size());
        }
        final SearchResult mainResult = results[0];
        long allRecordCount = mainResult.getAllRecordCount();

build --python_path="/usr/local/bin/python3"

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Use lld as the linker
build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code

	FNMSUB F1, F2, F3, F4           // fc8110fc
	FNMSUBCC F1, F2, F3, F4         // fc8110fd
	FNMSUBS F1, F2, F3, F4          // ec8110fc
	FNMSUBSCC F1, F2, F3, F4        // ec8110fd
	FSEL F1, F2, F3, F4             // fc8110ee
	FSELCC F1, F2, F3, F4           // fc8110ef
	FABS F1, F2                     // fc400a10
	FNABS F1, F2                    // fc400910
	FABSCC F1, F2                   // fc400a11

    gcc_env = get_gcc_compiler(environ_cp)
    if gcc_env is not None:

      # Use gold linker if 'gcc' and if 'ppc64le'
      write_to_bazelrc('build --linkopt="-fuse-ld=gold"')

      # Get the linker version
      ld_version = run_shell([gcc_env, '-Wl,-version']).split()

      ld_version_int = convert_version_to_int(ld_version[3])
      if ld_version_int is None:

build:release_linux_base --copt=-Wno-error=array-parameter
build:release_linux_base --copt=-Wno-error=unused-command-line-argument
# Set lld as the linker.
build:release_linux_base --linkopt="-fuse-ld=lld"
build:release_linux_base --linkopt="-lm"

# We have some invalid linker scripts in the build,
# so we need to disable this check
build:release_linux_base --linkopt=-Wl,--undefined-version

        metrics, preprocessing layers, and stateful RNN layers)
    *   Update Keras batch normalization layer to use the running mean and
        average computation in the `fused_batch_norm`. You should see
        significant performance improvements when using `fused_batch_norm` in
        Eager mode.

*   `tf.lite`:

    *   Enable TFLite experimental new converter by default.

*   XLA

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IEEE 754 standard; whether a <a href="#Run_time_panics">run-time panic</a>
occurs is implementation-specific.
</p>

<p>
An implementation may combine multiple floating-point operations into a single
fused operation, possibly across statements, and produce a result that differs
from the value obtained by executing and rounding the instructions individually.
An explicit floating-point type <a href="#Conversions">conversion</a> rounds to

IEEE 754 standard; whether a <a href="#Run_time_panics">run-time panic</a>
occurs is implementation-specific.
</p>

<p>
An implementation may combine multiple floating-point operations into a single
fused operation, possibly across statements, and produce a result that differs
from the value obtained by executing and rounding the instructions individually.

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