Apply tf1-tf2 renames to tensorflow/lite docstrings and comments.

No code changes, only doc-strings and comments.

PiperOrigin-RevId: 243841407

tensorflow/lite/experimental/examples/lstm/bidirectional_sequence_lstm_test.py

@@ -158,7 +158,7 @@ class BidirectionalSequenceLstmTest(test_util.TensorFlowTestCase):
       bw_lstm_layer: The backward lstm layer either a single lstm cell or a
         multi lstm cell.
       sess: Old session.
-      saver: saver created by tf.train.Saver()
+      saver: saver created by tf.compat.v1.train.Saver()
       is_dynamic_rnn: use dynamic_rnn or not.
 
     Returns:

tensorflow/lite/experimental/examples/lstm/bidirectional_sequence_rnn_test.py

@@ -184,7 +184,7 @@ class BidirectionalSequenceRnnTest(test_util.TensorFlowTestCase):
       bw_rnn_layer: The backward rnn layer either a single rnn cell or a multi
         rnn cell.
       sess: Old session.
-      saver: Saver created by tf.train.Saver()
+      saver: Saver created by tf.compat.v1.train.Saver()
       is_dynamic_rnn: Use dynamic_rnn or not.
       use_sequence_length: Whether to use sequence length or not. Default to
         False.

tensorflow/lite/experimental/examples/lstm/rnn.py

@@ -56,7 +56,7 @@ def dynamic_rnn(cell,
 
   ```python
   # create a BasicRNNCell
-  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size)
 
   # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]
 
@@ -64,22 +64,22 @@ def dynamic_rnn(cell,
   initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)
 
   # 'state' is a tensor of shape [batch_size, cell_state_size]
-  outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,
+  outputs, state = tf.compat.v1.nn.dynamic_rnn(rnn_cell, input_data,
                                      initial_state=initial_state,
                                      dtype=tf.float32)
   ```
 
   ```python
   # create 2 LSTMCells
-  rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]
+  rnn_layers = [tf.compat.v1.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]
 
   # create a RNN cell composed sequentially of a number of RNNCells
-  multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)
+  multi_rnn_cell = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_layers)
 
   # 'outputs' is a tensor of shape [batch_size, max_time, 256]
   # 'state' is a N-tuple where N is the number of LSTMCells containing a
-  # tf.contrib.rnn.LSTMStateTuple for each cell
-  outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
+  # tf.nn.rnn_cell.LSTMStateTuple for each cell
+  outputs, state = tf.compat.v1.nn.dynamic_rnn(cell=multi_rnn_cell,
                                      inputs=data,
                                      dtype=tf.float32)
   ```

tensorflow/lite/experimental/examples/lstm/unidirectional_sequence_lstm_test.py

@@ -143,7 +143,7 @@ class UnidirectionalSequenceLstmTest(test_util.TensorFlowTestCase):
     Args:
       lstm_layer: The lstm layer either a single lstm cell or a multi lstm cell.
       sess: Old session.
-      saver: Saver created by tf.train.Saver()
+      saver: Saver created by tf.compat.v1.train.Saver()
       is_dynamic_rnn: Use dynamic_rnn or not.
 
     Returns:

tensorflow/lite/experimental/examples/lstm/unidirectional_sequence_rnn_test.py

@@ -138,7 +138,7 @@ class UnidirectionalSequenceRnnTest(test_util.TensorFlowTestCase):
     Args:
       rnn_layer: The rnn layer either a single rnn cell or a multi rnn cell.
       sess: Old session.
-      saver: saver created by tf.train.Saver()
+      saver: saver created by tf.compat.v1.train.Saver()
       is_dynamic_rnn: use dynamic_rnn or not.
 
     Returns:

tensorflow/lite/python/convert.py

@@ -99,7 +99,7 @@ def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str):
   """Convert `input_data_str` according to model and toco parameters.
 
   Unless you know what you are doing consider using
-  the more friendly `tf.lite.toco_convert`.
+  the more friendly `tf.compat.v1.lite.toco_convert`.
 
   Args:
     model_flags_str: Serialized proto describing model properties, see

tensorflow/lite/python/op_hint.py

@@ -30,13 +30,13 @@ Example:
     output, = custom.add_outputs(output)
     return output
 
-  image = tf.placeholder(tf.float32, (1, 16, 16, 1))
+  image = tf.compat.v1.placeholder(tf.float32, (1, 16, 16, 1))
   output = tf.identity(tflite_cool_activation(image))
 
-  session = tf.Session()
+  session = tf.compat.v1.Session()
 
   graphdef_to_convert = tf.lite.convert_op_hints_to_stubs(session)
-  tflite_graph = tf.lite.toco_convert(graphdef_to_convert, [image], [output])
+  tflite_graph = tf.compat.v1.lite.toco_convert(graphdef_to_convert, [image], [output])
   with open("/tmp/graph.fb", "wb") as fp:
     fp.write(tflite_graph)
 

tensorflow/lite/python/util.py

@@ -149,7 +149,7 @@ def set_tensor_shapes(tensors, shapes):
 
 
 def get_grappler_config(enable_layout_optimizer=False, function_only=False):
-  """Creates a tf.ConfigProto for configuring Grappler.
+  """Creates a tf.compat.v1.ConfigProto for configuring Grappler.
 
   Args:
     enable_layout_optimizer: Bool indicating whether to run the layout

tensorflow/lite/testing/generate_examples_lib.py

@@ -625,7 +625,7 @@ def make_pool_tests(pool_op_in):
   """Make a set of tests to do average pooling.
 
   Args:
-    pool_op_in: TensorFlow pooling operation to test  i.e. `tf.nn.avg_pool`.
+    pool_op_in: TensorFlow pooling operation to test  i.e. `tf.nn.avg_pool2d`.
 
   Returns:
     A function representing the true generator (after curried pool_op_in).
@@ -3859,7 +3859,7 @@ def make_conv2d_transpose_tests(options):
 
 
 # Since compute output_shape is fairly complicated for
-# tf.nn.conv2d_backprop_input input_sizes argument, so we here first perform a
+# tf.nn.conv2d_transpose input_sizes argument, so we here first perform a
 # "conv2d" operation to get the output, then we use the output to feed in
 # tf.nn.conv2d_backprop_input.
 # This test will depend on the "conv2d" operation's correctness.
@@ -4567,7 +4567,7 @@ def make_reverse_sequence_tests(options):
 
 @register_make_test_function()
 def make_matrix_diag_tests(options):
-  """Make a set of tests for tf.matrix_diag op."""
+  """Make a set of tests for tf.linalg.diag op."""
 
   test_parameters = [
       {
@@ -4595,7 +4595,7 @@ def make_matrix_diag_tests(options):
 
 @register_make_test_function()
 def make_matrix_set_diag_tests(options):
-  """Make a set of tests for tf.matrix_set_diag op."""
+  """Make a set of tests for tf.linalg.set_diag op."""
 
   test_parameters = [
       {