def _batch_print(self, tensor_values):
if not tensor_values:
return
batch_size = tensor_values.values()[0].shape[0]
for i in range(min(self._first_k, batch_size)):
for k, v in tensor_values.items():
tf.logging.info("%s: %s", k, np.array_str(v[i]))
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