Support building model with dict inputs

Support already exists.
e79e90cb04/tensorflow/python/keras/engine/training.py (L377)

`valid_types` was updated to reflect that.

PiperOrigin-RevId: 334668220
Change-Id: I916ee0fcfb6871015751c47ca91a3d0102a6bb47

tensorflow/python/keras/engine/training.py

@@ -329,13 +329,13 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
     never throw unexpected errors in an unrelated workflow).
 
     Args:
-     input_shape: Single tuple, TensorShape, or list of shapes, where shapes
-         are tuples, integers, or TensorShapes.
+     input_shape: Single tuple, TensorShape, or list/dict of shapes, where
+         shapes are tuples, integers, or TensorShapes.
 
     Raises:
       ValueError:
         1. In case of invalid user-provided data (not of type tuple,
-           list, or TensorShape).
+           list, TensorShape, or dict).
         2. If the model requires call arguments that are agnostic
            to the input shapes (positional or kwarg in call signature).
         3. If not all layers were properly built.
@@ -351,7 +351,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
     if input_shape is None:
       raise ValueError('Input shape must be defined when calling build on a '
                        'model subclass network.')
-    valid_types = (tuple, list, tensor_shape.TensorShape)
+    valid_types = (tuple, list, tensor_shape.TensorShape, dict)
     if not isinstance(input_shape, valid_types):
       raise ValueError('Specified input shape is not one of the valid types. '
                        'Please specify a batch input shape of type tuple or '

tensorflow/python/keras/engine/training_test.py

@@ -3707,6 +3707,22 @@ class TestBuildCustomModel(keras_parameterized.TestCase):
     model.build([None, 1])
     self.assertEqual(model.l1.kernel.shape.as_list(), [1, 1])
 
+  @keras_parameterized.run_all_keras_modes
+  def test_build_dict_inputs(self):
+
+    class MyModel(training_module.Model):
+
+      def __init__(self):
+        super(MyModel, self).__init__()
+        self.l1 = layers_module.Dense(1)
+
+      def call(self, inputs):
+        return self.l1(inputs['x'])
+
+    model = MyModel()
+    model.build({'x': [None, 16]})
+    self.assertEqual(model.l1.kernel.shape.as_list(), [16, 1])
+
 
 if __name__ == '__main__':
   test.main()