extra_copts = ["-std=c11"],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":env",
        ":kernels",
    ],
)

tf_cuda_cc_test(
    name = "c_api_test",
    size = "medium",
    srcs = ["c_api_test.cc"],
    data = [
        ":test_op1.so",
        "//tensorflow/cc/saved_model:saved_model_half_plus_two",
    ],
    linkopts = select({

            size = vspace.NumElements(unaggregated_grads[0]);
            gradients_size.emplace(id, size);
          } else {
            size = size_it->second;
          }
          if (unaggregated_grads.size() * size * 4 > kMinAggregateBytes) {
            Gradient* grad = vspace.AggregateGradients(unaggregated_grads);
            unaggregated_grads.clear();
            unaggregated_grads.push_back(grad);
          }
        }

-	return s;
-    }
-  while (c != '\0');
-
- zero_fill:
-  do
-    *++s1 = '\0';
-  while (--n > 0);
-
-  return s;
+  size_t size = __strnlen (s2, n);
+  if (size != n)
+    memset (s1 + size, '\0', n - size);
+  return memcpy (s1, s2, size);
 }
 libc_hidden_builtin_def (strncpy)
diff --git a/sysdeps/gnu/bits/utmp.h b/sysdeps/gnu/bits/utmp.h
index aed2750..434a533 100644

#include "tensorflow/c/tf_status.h"

// Implementation of a filesystem for POSIX environments.
// This filesystem will support `file://` and empty (local) URI schemes.

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

// SECTION 1. Implementation for `TF_RandomAccessFile`
// ----------------------------------------------------------------------------

  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));
  memcpy(actual_values.get(), TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])

      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  flags->tf_xla_min_cluster_size = size;
}

TF_Buffer* TF_CreateConfig(unsigned char enable_xla_compilation,
                           unsigned char gpu_memory_allow_growth,

  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> size_op(
      TFE_NewOp(context.get(), "Size", status.get()), TFE_DeleteOp);
  auto result = parallel_device.Execute(
      context.get(), {unknown_dims_vector.get()}, "Size",
      TFE_OpGetAttrs(size_op.get()), 1, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  TF_ASSERT_OK((*result)[0]->Shape(&shape));
  EXPECT_EQ(0, shape->size());
}

// enabled through MarkForCompilationPassFlags.
TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled();

// Sets XLA's minimum cluster size. This has global effect.
TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than

          int64_t axis_length = combined_shape.dim_size(axis_index);
          if (axis_length != component_shape.dim_size(axis_index)) {
            axis_length = -1;
          }
          TF_RETURN_IF_ERROR(
              combined_shape.SetDimWithStatus(axis_index, axis_length));
        }
      }
    }
    auto dim_sizes = combined_shape.dim_sizes();
    shape_ = std::vector<int64_t>(dim_sizes.begin(), dim_sizes.end());
  }

    // tensor.
    if (inputs.size() != parallel_device.num_underlying_devices()) {
      std::string message(absl::StrCat(
          "The parallel device ", parallel_device_name, " expected ",
          parallel_device.num_underlying_devices(),
          " inputs to TPUReplicatedInput, but got ", inputs.size()));
      TF_SetStatus(status, TF_INVALID_ARGUMENT, message.c_str());
      return result;