def boosted_trees_make_stats_summary_eager_fallback(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function boosted_trees_make_stats_summary\n  '
    _ctx = (ctx if ctx else _context.context())
    if (not isinstance(bucketized_features_list, (list, tuple))):
        raise TypeError(("Expected list for 'bucketized_features_list' argument to 'boosted_trees_make_stats_summary' Op, not %r." % bucketized_features_list))
    _attr_num_features = len(bucketized_features_list)
    max_splits = _execute.make_int(max_splits, 'max_splits')
    num_buckets = _execute.make_int(num_buckets, 'num_buckets')
    node_ids = _ops.convert_to_tensor(node_ids, _dtypes.int32)
    gradients = _ops.convert_to_tensor(gradients, _dtypes.float32)
    hessians = _ops.convert_to_tensor(hessians, _dtypes.float32)
    bucketized_features_list = _ops.convert_n_to_tensor(bucketized_features_list, _dtypes.int32)
    _inputs_flat = ([node_ids, gradients, hessians] + list(bucketized_features_list))
    _attrs = ('max_splits', max_splits, 'num_buckets', num_buckets, 'num_features', _attr_num_features)
    _result = _execute.execute(b'BoostedTreesMakeStatsSummary', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('BoostedTreesMakeStatsSummary', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result
This is the slowpath function for Eager mode.
This is for function boosted_trees_make_stats_summary
def boosted_trees_make_stats_summary_eager_fallback(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function boosted_trees_make_stats_summary\n  '
    _ctx = (ctx if ctx else _context.context())
    if (not isinstance(bucketized_features_list, (list, tuple))):
        raise TypeError(("Expected list for 'bucketized_features_list' argument to 'boosted_trees_make_stats_summary' Op, not %r." % bucketized_features_list))
    _attr_num_features = len(bucketized_features_list)
    max_splits = _execute.make_int(max_splits, 'max_splits')
    num_buckets = _execute.make_int(num_buckets, 'num_buckets')
    node_ids = _ops.convert_to_tensor(node_ids, _dtypes.int32)
    gradients = _ops.convert_to_tensor(gradients, _dtypes.float32)
    hessians = _ops.convert_to_tensor(hessians, _dtypes.float32)
    bucketized_features_list = _ops.convert_n_to_tensor(bucketized_features_list, _dtypes.int32)
    _inputs_flat = ([node_ids, gradients, hessians] + list(bucketized_features_list))
    _attrs = ('max_splits', max_splits, 'num_buckets', num_buckets, 'num_features', _attr_num_features)
    _result = _execute.execute(b'BoostedTreesMakeStatsSummary', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('BoostedTreesMakeStatsSummary', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result