ij0zddlmZddlmZddlmZedGddZd dZed d gd Zy) )tree) keras_export) auto_namezkeras.KerasTensorcxeZdZdZ d=dZedZejdZedZejdZedZ e jd Z ed Z e jd Z ed Z e jd Z edZ e jdZ edZ dZd>dZdZdZdZdZd?dZdZdZdZdZdZdZdZdZd Zd!Zd"Zd#Z d$Z!d%Z"d&Z#d'Z$d(Z%d)Z&d*Z'd+Z(d,Z)d-Z*d.Z+d/Z,d0Z-d1Z.d2Z/d3Z0d4Z1d5Z2d6Z3d7Z4d8Z5d9Z6d:Z7d;Z8d>d<Z9y)@ KerasTensoraSymbolic tensor -- encapsulates a shape and a dtype. You can use `KerasTensor` instances to build computation graphs of Keras operations, such as `keras.Function` objects or Functional `keras.models.Model` objects. Example: >>> x = keras.KerasTensor(shape=(3, 4), dtype="float32") >>> x.shape (3, 4) >>> x.dtype float32 Calling a Keras operation (including a layer or a model) on a `KerasTensor` instance will return another `KerasTensor` instance with the appropriate shape and dtype. This is called a "symbolic call" (since there is no actual data involved). The computation of the correct output shape and dtype is called "static shape inference". Nc Jddlm}|jdd} |jdd} |r+tddj |j |j ||_|j||_ t||_ t||_ |jr|jr td| t| nd|_| |j| nd|_|xst#|j$j&|_||_y)Nr)backend ragged_rankrow_splits_dtypezUnexpected keyword arguments: z, zIKerasTensor cannot have `sparse=True` and `ragged=True` at the same time.) keras.srcr pop TypeErrorjoinkeysstandardize_shape_shapestandardize_dtype_dtypebool_sparse_ragged ValueErrorint _ragged_rank_row_splits_dtyper __class____name__namerecord_history) selfshapedtypesparseraggedrrkwargsr r r s z/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/backend/common/keras_tensor.py__init__zKerasTensor.__init__s &jj5 !::&8$? 06;;=1I0JK //6 //6 F| F| <Idnn&=&=> ,c|jSN)rr s r&r!zKerasTensor.shapeE {{r(ctd)NzlThe `shape` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.AttributeErrorr values r&r!zKerasTensor.shapeI =  r(c|jSr*)rr+s r&r"zKerasTensor.dtypePr,r(ctd)NzlThe `dtype` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.r.r0s r&r"zKerasTensor.dtypeTr2r(c|jSr*)rr+s r&r#zKerasTensor.sparse[ ||r(ctd)NzmThe `sparse` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.r.r0s r&r#zKerasTensor.sparse_r2r(c|jSr*)rr+s r&r zKerasTensor.ragged_rankfs   r(ctd)NzrThe `ragged_rank` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.r.r0s r&r zKerasTensor.ragged_rankj D  r(c|jSr*)rr+s r&r zKerasTensor.row_splits_dtypeqs%%%r(ctd)NzwThe `row_splits_dtype` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.r.r0s r&r zKerasTensor.row_splits_dtypeur:r(c|jSr*)rr+s r&r$zKerasTensor.ragged|r6r(ctd)NzmThe `ragged` attribute of KerasTensor is immutable. One should create a new instance of KerasTensor for this.r.r0s r&r$zKerasTensor.raggedr2r(c,t|jSr*)lenr!r+s r&ndimzKerasTensor.ndims4::r(c<ddlm}|j||SNrops)r rEReshape)r newshaperEs r&reshapezKerasTensor.reshapes!$s{{8$T**r(c<ddlm}|j||SrC)r rESqueeze)r axisrEs r&squeezezKerasTensor.squeezes! s{{4 &&r(ctd)NzA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to an int.rr+s r&__int__zKerasTensor.__int__s /  r(ctd)NzA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a float.rNr+s r& __float__zKerasTensor.__float__s 0  r(ctd)NzA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.rNr+s r& __array__zKerasTensor.__array__s 6  r(ctd)Na%A KerasTensor cannot be used as input to a JAX function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.ops`). You are likely doing something like: ``` x = Input(...) ... jax_fn(x) # Invalid. ``` What you should do instead is wrap `jax_fn` in a layer: ``` class MyLayer(Layer): def call(self, x): return jax_fn(x) x = MyLayer()(x) ``` rNr+s r& __jax_array__zKerasTensor.__jax_array__   r(ctd)Na)A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.ops`). You are likely doing something like: ``` x = Input(...) ... tf_fn(x) # Invalid. ``` What you should do instead is wrap `tf_fn` in a layer: ``` class MyLayer(Layer): def call(self, x): return tf_fn(x) x = MyLayer()(x) ``` rN)r r"rs r& __tf_tensor__zKerasTensor.__tf_tensor__rVr(c d|jd|jd|jd|jd|jd S)Nz)r!r"r#r$rr+s r&__repr__zKerasTensor.__repr__sG!$**Xdjj\Bkk])DKK= {! M r(ctd)Nz7Iterating over a symbolic KerasTensor is not supported.)NotImplementedErrorr+s r&__iter__zKerasTensor.__iter__s! E  r(ctd)Nz3A symbolic KerasTensor cannot be used as a boolean.)rr+s r&__bool__zKerasTensor.__bool__sMNNr(cNddlm}|jj||SrCr rEAdd symbolic_callr otherrEs r&__add__zKerasTensor.__add__!wwy&&tU33r(cNddlm}|jj||SrCrbres r&__radd__zKerasTensor.__radd__!wwy&&ud33r(cNddlm}|jj||SrCr rESubtractrdres r&__sub__zKerasTensor.__sub__!||~++D%88r(cNddlm}|jj||SrCrmres r&__rsub__zKerasTensor.__rsub__!||~++E488r(cNddlm}|jj||SrCr rEMultiplyrdres r&__mul__zKerasTensor.__mul__rpr(cNddlm}|jj||SrCrures r&__rmul__zKerasTensor.__rmul__rsr(cNddlm}|jj||SrCr rEMatmulrdres r& __matmul__zKerasTensor.__matmul__!zz|))$66r(cNddlm}|jj||SrCr{res r& __rmatmul__zKerasTensor.__rmatmul__ !zz|))%66r(cNddlm}|jj||SrCr rEDividerdres r&__div__zKerasTensor.__div__r~r(cNddlm}|jj||SrCrres r&__rdiv__zKerasTensor.__rdiv__rr(cNddlm}|jj||SrCr rE TrueDividerdres r& __truediv__zKerasTensor.__truediv__!~~--dE::r(cNddlm}|jj||SrCrres r& __rtruediv__zKerasTensor.__rtruediv__!~~--eT::r(cLddlm}|jj|SrC)r rENegativerdr rEs r&__neg__zKerasTensor.__neg__"!||~++D11r(cLddlm}|jj|SrC)r rEAbsoluterdrs r&__abs__zKerasTensor.__abs__'rr(cNddlm}|jj||SrCr rEPowerrdres r&__pow__zKerasTensor.__pow__,s!yy{((u55r(cNddlm}|jj||SrCrres r&__rpow__zKerasTensor.__rpow__1s!yy{((55r(cNddlm}|jj||SrCr rE FloorDividerdres r& __floordiv__zKerasTensor.__floordiv__6s! ..tU;;r(cNddlm}|jj||SrCrres r& __rfloordiv__zKerasTensor.__rfloordiv__;s! ..ud;;r(cNddlm}|jj||SrCr rEModrdres r&__mod__zKerasTensor.__mod__@rhr(cNddlm}|jj||SrCrres r&__rmod__zKerasTensor.__rmod__Erkr(cNddlm}|jj||SrC)r rELessrdres r&__lt__zKerasTensor.__lt__Js!xxz''e44r(cNddlm}|jj||SrC)r rE LessEqualrdres r&__le__zKerasTensor.__le__O!}},,T599r(cNddlm}|jj||SrC)r rEGreaterrdres r&__gt__zKerasTensor.__gt__Ts!{{}**477r(cNddlm}|jj||SrC)r rE GreaterEqualrdres r&__ge__zKerasTensor.__ge__Ys!!!//e<rsZ-,!"H@H@#H@V ,.MNO &P &r(