o ?eg @sXdZddlmZddlmZddlmZddlmZddlmZddZd Z d d Z d S) zTensor shape utilities.)context)dtypes)ops) tensor_shape) tensor_utilcCs>d}t|ttfr|stj}ntttj|}tj ||ddS)zBConvert to an int32 or int64 tensor, defaulting to int32 if empty.Nshape)dtypename) isinstancetuplelistrZint32maprZdimension_valuerZconvert_to_tensor)rrra/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/shape_util.py shape_tensors rTcCs^tr%ts'tjr)|js+t |r-t |}t |}| |dSdSdSdSdSdS)a&Sets the shape of `tensor` to the `shape`'s constant value, if inferrable. This is a temporary workaround to fix shape inference across functional op boundaries. E.g. ```python shape = tf.constant([3]) @tf.function def f(): u = tf.random_uniform(shape) return u ``` If we were to rely solely on C++ shape inference, the shape of `u` inside `f` would be unknown because C++ shape inference is not aware of the outer graph and all it sees is a Placeholder node when backtracing the captured tensor for `shape`. `maybe_set_static_shape` computes the static shape value of `shape` by traversing the `FuncGraph` boundaries and sets the correct shape. A longer term solution would be to fix C++ shape inference. Args: tensor: A tensor. shape: A shape tensor. N) _ENABLE_MAYBE_SET_STATIC_SHAPErZexecuting_eagerlyrZget_default_graphZbuilding_functionrZis_fully_definedrZ is_tensorrZconstant_value_as_shape set_shape)ZtensorrZ const_shaperrrmaybe_set_static_shape*s  rN) __doc__Ztensorflow.python.eagerrZtensorflow.python.frameworkrrrrrrrrrrrs