AVh dZddlmZddlmZddlmZddlmZddlm Z ddl m Z e jdg d d Z y ) zConfig functions for TF NumPy.)ops)weak_tensor_ops) np_dtypes) np_math_ops) tf_logging) tf_exportz5experimental.numpy.experimental_enable_numpy_behavior)v1c|dk(s|dk(rtjdtj|tjt j tj|y)aEnable NumPy behavior on Tensors. Enabling NumPy behavior has three effects: * It adds to `tf.Tensor` some common NumPy methods such as `T`, `reshape` and `ravel`. * It changes dtype promotion in `tf.Tensor` operators to be compatible with NumPy. For example, `tf.ones([], tf.int32) + tf.ones([], tf.float32)` used to throw a "dtype incompatible" error, but after this it will return a float64 tensor (obeying NumPy's promotion rules). * It enhances `tf.Tensor`'s indexing capability to be on par with [NumPy's](https://numpy.org/doc/stable/reference/arrays.indexing.html). Args: prefer_float32: Controls whether dtype inference will use float32 for Python floats, or float64 (the default and the NumPy-compatible behavior). dtype_conversion_mode: a string that specifies promotion mode. This string corresponds to a PromoMode Enum and can be 'off', 'legacy', 'safe', or 'all'. 'safe' or 'all' mode enables the auto dtype conversion semantics. safeallzUserWarning: enabling the new type promotion must happen at the beginning of the program. Please ensure no TF APIs have been used yet.N) rwarningrset_dtype_conversion_modeenable_numpy_style_slicingrenable_numpy_methods_on_tensorrset_prefer_float32)prefer_float32dtype_conversion_modes Y/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/numpy_ops/np_config.pyenable_numpy_behaviorrs^0f$(=(F   56  " ,,. ~.N)Flegacy) __doc__tensorflow.python.frameworkrtensorflow.python.opsrtensorflow.python.ops.numpy_opsrrtensorflow.python.platformrtensorflow.python.utilrrrrrsB%+1571,;//r