def boosted_trees_predict_eager_fallback(tree_ensemble_handle, bucketized_features, logits_dimension, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function boosted_trees_predict\n  '
    _ctx = (ctx if ctx else _context.context())
    if (not isinstance(bucketized_features, (list, tuple))):
        raise TypeError(("Expected list for 'bucketized_features' argument to 'boosted_trees_predict' Op, not %r." % bucketized_features))
    _attr_num_bucketized_features = len(bucketized_features)
    logits_dimension = _execute.make_int(logits_dimension, 'logits_dimension')
    tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
    bucketized_features = _ops.convert_n_to_tensor(bucketized_features, _dtypes.int32)
    _inputs_flat = ([tree_ensemble_handle] + list(bucketized_features))
    _attrs = ('num_bucketized_features', _attr_num_bucketized_features, 'logits_dimension', logits_dimension)
    _result = _execute.execute(b'BoostedTreesPredict', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('BoostedTreesPredict', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result
This is the slowpath function for Eager mode.
This is for function boosted_trees_predict
def boosted_trees_predict_eager_fallback(tree_ensemble_handle, bucketized_features, logits_dimension, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function boosted_trees_predict\n  '
    _ctx = (ctx if ctx else _context.context())
    if (not isinstance(bucketized_features, (list, tuple))):
        raise TypeError(("Expected list for 'bucketized_features' argument to 'boosted_trees_predict' Op, not %r." % bucketized_features))
    _attr_num_bucketized_features = len(bucketized_features)
    logits_dimension = _execute.make_int(logits_dimension, 'logits_dimension')
    tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
    bucketized_features = _ops.convert_n_to_tensor(bucketized_features, _dtypes.int32)
    _inputs_flat = ([tree_ensemble_handle] + list(bucketized_features))
    _attrs = ('num_bucketized_features', _attr_num_bucketized_features, 'logits_dimension', logits_dimension)
    _result = _execute.execute(b'BoostedTreesPredict', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('BoostedTreesPredict', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result