def random_uniform(minval, maxval, dims, name=None): minval = ops.convert_to_tensor(minval) return random_ops.random_uniform(dims, minval, maxval, dtype=minval.dtype, name=name) recv = gen_xla_ops.xla_recv reduce = gen_xla_ops.xla_reduce variadic_reduce = gen_xla_ops.xla_variadic_reduce ops.no_gradient("XlaVariadicReduce") def reduce_window(operand, init, reducer, window_dimensions, window_strides=None, base_dilations=None, window_dilations=None, padding=None, name=None): """Wraps the XLA ReduceWindow operator. ReduceWindow is documented at https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
[dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]) result = gen_ragged_math_ops.ragged_range( starts, limits, deltas, Tsplits=row_splits_dtype, name=name) return ragged_tensor.RaggedTensor.from_row_splits( result.rt_dense_values, result.rt_nested_splits, validate=False) def _infer_matching_dtype(tensors, dtype_hierarchy): """Infers a matching dtype for tensors, and casts them to that dtype.""" assert all(t.dtype in dtype_hierarchy for t in tensors) inferred_dtype = max([t.dtype for t in tensors], key=dtype_hierarchy.index) return [math_ops.cast(t, inferred_dtype) for t in tensors] ops.no_gradient('RaggedRange') #=============================================================================== # ragged_segment_<AGGREGATE> #=============================================================================== # Docstring template used for the raggged_segment_<AGGREGATE> ops. _RAGGED_SEGMENT_DOCSTRING = """\ Computes the %(combination)s along segments of a RaggedTensor. Returns a RaggedTensor `output` with `num_segments` rows, where the row `output[i]` is formed by taking the %(combination)s of all rows of `data` whose corresponding `segment_id` is `i`. The length of the row `output[i]` will be the maximum of the lengths of all rows of `data` whose corresponding `segment_id` is `i`. If no `data`