diff --git a/tf_agents/utils/nest_utils_test.py b/tf_agents/utils/nest_utils_test.py
index c7ede7f09..adcb91f7a 100644
--- a/tf_agents/utils/nest_utils_test.py
+++ b/tf_agents/utils/nest_utils_test.py
@@ -97,6 +97,28 @@ def zeros_from_spec(self, spec, batch_size=None, extra_sizes=None):
 
     return tf.nest.pack_sequence_as(spec, tensors)
 
+  def placeholders_from_spec(self, spec):
+    """Return tensors matching spec with an added unknown batch dimension.
+
+    Args:
+      spec: A `tf.TypeSpec`, e.g. `tf.TensorSpec` or `tf.SparseTensorSpec`.
+
+    Returns:
+      A possibly nested tuple of Tensors matching the spec.
+    """
+    tensors = []
+    for s in tf.nest.flatten(spec):
+      if isinstance(s, tf.SparseTensorSpec):
+        raise NotImplementedError(
+            'Support for SparseTensor placeholders not implemented.')
+      elif isinstance(s, tf.TensorSpec):
+        shape = tf.TensorShape([None]).concatenate(s.shape)
+        tensors.append(tf.placeholder(dtype=s.dtype, shape=shape))
+      else:
+        raise TypeError('Unexpected spec type: {}'.format(s))
+
+    return tf.nest.pack_sequence_as(spec, tensors)
+
   def testGetOuterShapeNotBatched(self):
     tensor = tf.zeros([2, 3], dtype=tf.float32)
     spec = tensor_spec.TensorSpec([2, 3], dtype=tf.float32)
@@ -522,6 +544,22 @@ def testFlattenMultiBatchedNestedTensors(self):
     batch_dims_ = self.evaluate(batch_dims)
     self.assertAllEqual(batch_dims_, [7, 5])
 
+  def testFlattenMultiBatchedNestedTensorsWithPartiallyKnownShape(self):
+    if tf.executing_eagerly():
+      self.skipTest('Do not check nest processing of data in eager mode. '
+                    'Placeholders are not compatible with eager execution.')
+    shape = [2, 3]
+    specs = self.nest_spec(shape, include_sparse=False)
+    tensors = self.placeholders_from_spec(specs)
+
+    (batch_flattened_tensors,
+     _) = nest_utils.flatten_multi_batched_nested_tensors(
+         tensors, specs)
+
+    tf.nest.assert_same_structure(specs, batch_flattened_tensors)
+    assert_shapes = lambda t: self.assertEqual(t.shape.as_list(), [None, 2, 3])
+    tf.nest.map_structure(assert_shapes, batch_flattened_tensors)
+
 
 class NestedArraysTest(tf.test.TestCase):
   """Tests functions related to nested arrays."""