Hi, thank you for developing this great project.

I’m having trouble understanding how to use a class with a TensorNode. Specifically I can’t figure out how to set up a class that takes a batch of inputs; the example in the documentation only uses one image.

I’ve tried to make a minimal example that reproduces my issue.

```
MNISTY_SHAPE = (28, 28, 1)
SIZE_OUT = 10
class SimpleNode:
def __call__(self, t, x):
img = tf.reshape(tf.cast(x, tf.float32), (-1,) + MNISTY_SHAPE)
conv1 = tf.layers.conv2d(img, filters=32, kernel_size=(5,5), strides=(3,3), padding='VALID')
maxpool1 = tf.layers.max_pooling2d(conv1,
pool_size=(2,2),
strides=(2,2),
padding='VALID')
input_shape = maxpool1.get_shape().as_list()[1:]
n_input_units = np.prod(input_shape)
n_output_units = SIZE_OUT
weights_shape = [n_input_units, n_output_units]
fc1W = tf.get_variable(name='fc1W_weights',
shape=weights_shape)
fc1b = tf.get_variable(name='fc1W_biases',
initializer=lambda shape, dtype, partition_info: tf.zeros(shape=shape, dtype=dtype),
shape=[n_output_units])
fc1 = tf.nn.relu_layer(
tf.reshape(maxpool1, [-1, int(np.prod(maxpool1.get_shape()[1:]))]), fc1W, fc1b)
probabilities = tf.nn.softmax(fc1, name='probabilities')
net = nengo.Network()
with net:
input_shape = np.prod(MNISTY_SHAPE)
input_node = nengo.Node(output=np.zeros(MNISTY_SHAPE).flatten())
simplenode = nengo_dl.TensorNode(SimpleNode(),size_in=input_shape,size_out=SIZE_OUT)
nengo.Connection(input_node, simplenode, synapse=None)
minibatch_size = 20
sim = nengo_dl.Simulator(net, minibatch_size=minibatch_size)
```

When I run this, I get a crash, I think it’s from the Tensorflow “VM” after the sim has been built.

```
# ...(very long traceback from my_environment/site-packages/tensorflow)...
ValueError: Tried to convert 'x' to a tensor and failed. Error: None values not supported.
```

Is there something I’m doing wrong in setting up the `__call__`

function of the class?

I’m guessing it’s because there’s a `None`

in the shape of the tensor being passed as `x`

?