Update Keras to use public TF API to retrieve the build info.

PiperOrigin-RevId: 339309293
Change-Id: I68180d802294ab1dbc53837e7219a2c04f6043c8

Author: qlzh727

tensorflow/python/keras/layers/BUILD

@@ -421,7 +421,6 @@ py_library(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:nn",
         "//tensorflow/python:platform",
-        "//tensorflow/python:platform_build_info",
         "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:util",

tensorflow/python/keras/layers/recurrent_v2.py

@@ -39,7 +39,7 @@
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
-from tensorflow.python.platform import build_info
+from tensorflow.python.platform import sysconfig
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export
 
@@ -650,7 +650,7 @@ def gpu_gru(inputs, init_h, kernel, recurrent_kernel, bias, mask, time_major,
   # (6 * units)
   bias = array_ops.split(K.flatten(bias), 6)
 
-  if build_info.build_info['is_cuda_build']:
+  if sysconfig.get_build_info()['is_cuda_build']:
     # Note that the gate order for CuDNN is different from the canonical format.
     # canonical format is [z, r, h], whereas CuDNN is [r, z, h]. The swap need
     # to be done for kernel, recurrent_kernel, input_bias, recurrent_bias.
@@ -1454,7 +1454,7 @@ def gpu_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask,
   # so that mathematically it is same as the canonical LSTM implementation.
   full_bias = array_ops.concat((array_ops.zeros_like(bias), bias), 0)
 
-  if build_info.build_info['is_rocm_build']:
+  if sysconfig.get_build_info()['is_rocm_build']:
     # ROCm MIOpen's weight sequence for LSTM is different from both canonical
     # and Cudnn format
     # MIOpen: [i, f, o, c] Cudnn/Canonical: [i, f, c, o]