title: Why doesn't TensorFlow support Boosted Decision Trees with Dense Tensors of int64?
body: <p>The relevant file is: <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py" rel="nofollow noreferrer">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py</a> lines 236-262</p>
  
  <pre><code>    if isinstance(tensor, sparse_tensor.SparseTensor):
        if tensor.values.dtype == dtypes.float32:
          sparse_float_names.append(key)
          sparse_float_indices.append(tensor.indices)
          sparse_float_values.append(tensor.values)
          sparse_float_shapes.append(tensor.dense_shape)
        elif tensor.values.dtype == dtypes.int64:
          sparse_int_names.append(key)
          sparse_int_indices.append(tensor.indices)
          sparse_int_values.append(tensor.values)
          sparse_int_shapes.append(tensor.dense_shape)
        else:
          raise ValueError("Unsupported sparse feature %s with dtype %s." %
                           (tensor.indices.name, tensor.dtype))
      else:
        if tensor.dtype == dtypes.float32:
          if len(tensor.shape) &gt; 1 and tensor.shape[1] &gt; 1:
            unstacked = array_ops.unstack(tensor, axis=1)
            for i in range(len(unstacked)):
              dense_float_names.append(_FEATURE_NAME_TEMPLATE % (key, i))
              dense_floats.append(array_ops.reshape(unstacked[i], [-1, 1]))
          else:
            dense_float_names.append(key)
            dense_floats.append(tensor)
        else:
          raise ValueError("Unsupported dense feature %s with dtype %s." %
                           (tensor.name, tensor.dtype))
  </code></pre>
  
  <p>We can see that sparse int64 tensors are supported but in the else clause starting on we can see that for dense tensors, only float32 type is supported. Why is this?</p>