+ j^RIt^RIt^RIt^RIHt^RIt^RIt^RIH t ^RI H t H t H t ^RIHt^RIHt^RIHt^RIHt^R IHt^R IHt^R IHtHt.RDOt]P<!]4t R R lt!RRlt"RRlt#RRlt$RRlt%/]PLRR]#3b]PNRR]#3b]PPRR]#3b]PRRR]%3b]PTRR]%3b]PVRR]%3b]PXRR]%3b]PZRR]%3b]P\RR]%3b]P^RR]%3b]P`R R]%3b]PbR R]%3b]PdR!R]%3b]PfR!R]%3b]PhR!R]%3b]PjR"R#]%3b]PlR$R%]$3b]PnR$R%]$3]PpR&R%]$3]PrR&R%]$3]PtR'R(]%3]PvR)R*]$3]PxR)R*]$3]P\RR+]%3]PzRR+]%3]P|RR+]%3/ Ct?R,R-lt@RER.ltARFR/ltBRGR0ltCR1tDR2tERHR3ltFRHR4ltGRIR5ltHRJR6ltIRHR7ltJRKR8ltKRLR9ltLR:tMRMR;ltNR<tOR=tPRNR>ltQRNR?ltRROR@ltSRAtTRBtURORCltVR#)PN)Any) struct_pb2)HistogramProtoSummarySummaryMetadata) TensorProto)TensorShapeProto) layout_pb2)PrCurvePluginData)TextPluginData)make_np)_prepare_videoconvert_to_HWCc0V^8dQhR\R\/#)freturn)floatint)formats"`/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/utils/tensorboard/summary.py __annotate__r6s & &5 &S &cl\P!RV4p\P!RV4^,#)a-Casts a half-precision float value into an integer. Converts a half precision floating point value, such as `torch.half` or `torch.bfloat16`, into an integer value which can be written into the half_val field of a TensorProto for storage. To undo the effects of this conversion, use int_to_half(). ristructpackunpack)rbufs& r half_to_intr!6s* ++c1 C ==c "1 %%rc0V^8dQhR\R\/#)rrr)rr)rs"rrrCs&&3&5&rcl\P!RV4p\P!RV4^,#)zCasts an integer value to a half-precision float. Converts an integer value obtained from half_to_int back into a floating point value. rrr)rr s& r int_to_halfr$Cs* ++c1 C ==c "1 %%rcZV^8dQhR\PR\\,/#rtr)torchTensorlistr)rs"rrrMs"::5<<:DI:rczVP4P4Uu.uFp\V4NK up#uupiN)flattentolistr!)r'xs& r_tensor_to_half_valr0Ms/$%IIK$6$6$8 9$8qKN$8 99 9s8cZV^8dQhR\PR\\,/#r&)r(r)r*r)rs"rrrPs"44ell4tE{4rcf\P!V4P4P4#r,)r( view_as_realr-r.r's&r_tensor_to_complex_valr5Ps%   a ( ( * 1 1 33rcZV^8dQhR\PR\\,/#r&)r(r)r*r)rs"rrrSs"  u|| S  rc>VP4P4#r,)r-r.r4s&r_tensor_to_listr8Ss 99;   rDT_HALFhalf_val DT_BFLOAT16DT_FLOAT float_val DT_DOUBLE double_valDT_INT8int_valDT_UINT8DT_INT16DT_INT32DT_INT64 int64_val DT_COMPLEX32 scomplex_val DT_COMPLEX64DT_BOOLbool_val DT_COMPLEX128 dcomplex_val uint32_valc$V^8dQhR\/#)rr)r)rs"rrrus55#5rc\V\P4'gVP4MTpVP\P 8Xd^#^#)) isinstancenpndarraynumpydtypeuint8)tensor converteds& r_calc_scale_factorrZus9&0&D&D &I288+144rc ^RIHp Hp V P4p V P V4p WW$3wrppV P W3V V3VV3W3W3.VVR7V'dTpV P V4wppppVV, VV, pp\P!RV,4pV PV VV, ^V,, 3V V,V3.VR7V PV V,VV, V, 3VVV R7V#)r) ImageDraw ImageFont)widthfillg?)r_)r_font) PILr\r] load_defaultDrawlinegetbboxrSceil rectangletext)imagexminyminxmaxymax display_strcolor color_text thicknessr\r]r`drawleftrighttopbottom text_bottom_left_top_right_bottom text_width text_heightmargins&&&&&&&&& r_draw_single_boxrzs#)  ! ! #D >>% D"&d!9T#vII tVnufo|d[Q   '+||K'@$tVW"(5.'D.K  +, {[01v:=> "K0    F]K+5> ?   Lrc  aa^RIp^RIHpHpHpHpHpHp ^RIH p H p H p H p H p^RIHpHpHp\%S\&4'g!\(P+R4\-R4h\%V\&4'g!\(P+R4\-R4hT;'g/p\%V\&4'g \-R4hVP/4FwwopSS9g[\%V\04'dE\2;QJd VV3RlV4F 'dK R M R M!VV3RlV44'dKg\-R S R S R 24h .pV!4pSP/4EFwopVfK \%V\4\634'dVVP8S,nSV9dE\<P>!VS,Uu.uFp\<P@!VR7NK upR7pMRpVPCV!SVPAR4VR74K\%V\D4'dVVP8S,n#SV9dE\<P>!VS,Uu.uFp\<P@!VR7NK upR7pMRpVPCV!SVPAR4VR74EK`\%V\H4'dVVP8S,n%SV9dE\<P>!VS,Uu.uFp\<P@!VR7NK upR7pMRpVPCV!SVPAR4VR74EK\%VVPL4'dV\OV4^,pVVP8S,nVPCV!SVPAR4R74EKx\QR4h V!VV R7p\S\RPT!V VPW4R7R7p\Y\XP@!VVR7.R7pVUu.uFpV!V!VR7R7NK ppV!VVR7pV!VV R 7p\S\RPT!V VPW4R7R7p\Y\XP@!V VR7.R7pV!V PAR!4R"7pV!VV R#7p\S\RPT!V VPW4R7R7p\Y\XP@!V VR7.R7pVVV3#uupiuupiuupiuupi)$aOutput three `Summary` protocol buffers needed by hparams plugin. `Experiment` keeps the metadata of an experiment, such as the name of the hyperparameters and the name of the metrics. `SessionStartInfo` keeps key-value pairs of the hyperparameters `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS Args: hparam_dict: A dictionary that contains names of the hyperparameters and their values. metric_dict: A dictionary that contains names of the metrics and their values. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold Returns: The `Summary` protobufs for Experiment, SessionStartInfo and SessionEndInfo N)DataType Experiment HParamInfo MetricInfo MetricNameStatus)EXPERIMENT_TAGPLUGIN_DATA_VERSION PLUGIN_NAMESESSION_END_INFO_TAGSESSION_START_INFO_TAG)HParamsPluginDataSessionEndInfoSessionStartInfoz>parameter: hparam_dict should be a dictionary, nothing logged.z>parameter: metric_dict should be a dictionary, nothing logged.zIparameter: hparam_domain_discrete should be a dictionary, nothing logged.c3\<"TF!p\V\SS,44xK# R#5ir,)rRtype).0d hparam_dictks& r hparams..s$FAqz!T+a.%9::As),FTz"parameter: hparam_domain_discrete[z/] should be a list of same type as hparam_dict[z].) number_value)valuesDATA_TYPE_FLOAT64)namerdomain_discrete) string_valueDATA_TYPE_STRING) bool_valueDATA_TYPE_BOOL)rrz=value should be one of int, float, str, bool, or torch.Tensor)session_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluer)r) hparam_infos metric_infos) experimentrSTATUS_SUCCESS)status)session_end_infor)-r(#tensorboard.plugins.hparams.api_pb2rrrrrr$tensorboard.plugins.hparams.metadatarrrrr+tensorboard.plugins.hparams.plugin_data_pb2rrrrRdictloggerwarning TypeErroritemsr*allrrhparamsrr ListValueValueappendstrrboolrr)r ValueErrorr PluginDataSerializeToStringr)r metric_dicthparam_domain_discreter(rrrrrrrrrrrrrrvhpsssirrrsmdrmtsexpseisf&& ` rrrs( k4 ( (WX L   k4 ( (WX L  499r ,d 3 3 W  ',,.1 [ a&&3FAF333FAFFF4QC7fghfiikl  / C  C!!#1 9  a#u & &*+CKKN '**?I?S?S"8!:!:A#((a8!:@#' JJ!(;<$3   a  *+CKKN '**","6"6"8!:!:A#((a8!:##' JJ!(:;$3   a  ()CKKN %**","6"6"8!:!:A#((A6!:##' JJ!(89$3   a & & 1 A*+CKKN ' JJzqx~~>Q/RS T  K  U$\ 3@STG #..#W-F-F-H  C +ACPQ RC7B C{!::!, -{C C #C 8C38KLG #..#W-F-F-H  C >CHI JC  -= > ?C>QRG #..#W-F-F-H  C +?#NO PC S=G,,D Ds U 5 U  U Uc\V4P4pVP^8wd'\RVP RVP R24h\ V4pV'dl\ V.RR7pV'd\ V.RR7p\P!RR 7p\VR 7p\\P!VVVR 7.R 7#\\P!WR 7.R 7#)aOutput a `Summary` protocol buffer containing a single scalar value. The generated Summary has a Tensor.proto containing the input Tensor. Args: name: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: A real numeric Tensor containing a single value. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. new_style: Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. Returns: A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf. Raises: ValueError: If tensor has the wrong shape or type. zNTensor should contain one element (0 dimensions). Was given size: z and z dimensions.r<)r=rVr>)r?rVscalarsrrrrXrr)r simple_value) r squeezendimAssertionErrorsizerrrrrr) rrX collections new_styledouble_precisionscalar tensor_protorrs &&&&& rrrbs"V_ $ $ &F {{a #[[Mv{{m<IJ J6]F"fXZH &6(+NL%00YG +6 '   gmmJKLLrc,VP4VP,R 8d \R4hVP\9dk\VP,wr#p\ R /RTR\ VPUu.uFp\ P!VR7NK upR7W4!V4/BpM\V RVP 24h\P!RR7p\VR 7p\\P!WVR 7.R 7#uupi)aOutputs a `Summary` protocol buffer containing the full tensor. The generated Summary has a Tensor.proto containing the input Tensor. Args: tag: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: Tensor to be converted to protobuf Returns: A tensor protobuf in a `Summary` protobuf. Raises: ValueError: If tensor is too big to be converted to protobuf, or tensor data type is not supported zAtensor is bigger than protocol buffer's hard limit of 2GB in sizerV tensor_shaperdimz has unsupported tensor dtype rXrrrrrXrl) numelitemsizerrV_TENSOR_TYPE_MAPrr shapeDimrrrr) rrXrV field_name conversion_fnr/rrrs && rrrs||~'G4 O  ||''+;FLL+I(="  0?E||L|!)--15|L!M&1  C5 >v||nMNN!,,BK k 2C '--ClST UUMs> Dc j\VVVVVVVR7p\\P!WR7.R7#)aOutput a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. min: A float or int min value max: A float or int max value num: Int number of values sum: Float or int sum of all values sum_squares: Float or int sum of squares for all values bucket_limits: A numeric `Tensor` with upper value per bucket bucket_counts: A numeric `Tensor` with number of values per bucket Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. minmaxnumsum sum_squares bucket_limitbucketrhistor)rrr) rrrrrr bucket_limits bucket_countshists &&&&&&&& r histogram_rawrs<*     " D '--D=> ??rc\V4p\VP\4W#4p\ \P !WR7.R7#)a0Output a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. This op reports an `InvalidArgument` error if any value is not finite. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. values: A real numeric `Tensor`. Any shape. Values to use to build the histogram. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. rr)r make_histogramastyperrr)rrbinsmax_binsrs&&&& r histogramrs9"V_F &--. ?D '--D=> ??rc VP^8Xd \R4hVPR 4p\P!WR7wr4\ V4pVeWR8dWR,pWV,pV^8wd$\P !V^Wg, ..R^R7pVPR V4PR R7p\P!VP^,3VP4pVRR V1,VRR %VR ,VR &Tp\P!\P!V^44p \P!V ^V R ,^, .RR7wr\V 4p \V 4^,p ? V ^8d W:^, V M\P!^.VRV .4pWJV ^,pVP^8XgVP^8Xd \R 4hVPV4p \!VP#4VP%4\ V4VP4V VP'4VP'4R 7#) zDConvert values into a histogram proto using logic from histogram.cc.zThe input has no element.)rNconstant) pad_widthmodeconstant_values)axisrt)sidez1The histogram is empty, please file a bug report.r)rrreshaperSrlenpadremptyrVcumsumgreater searchsortedr concatenatedotrrrr.) rrrcountslimitsnum_bins subsamplingsubsampling_remainder new_limits cum_countsstartendsum_sqs &&& rrrs {{a455 ^^B F\\&4NF6{H 3* ( 6 A %VV{BCD ! F K044"4=XXv{{Q0&,,?  "[1 3B 22::fa01JaB!1C-D7SJE JE c(Q,C$)19qy3"..1#vds|AT2U C!G $F {{a6;;!+LMM ZZ F  JJL JJL K JJL]]_}} rcH\V4p\W4p\V4pVP\P 4pW,P ^^4P\P4p\WR7p\\P!WR7.R7#)aOutput a `Summary` protocol buffer with images. The summary has up to `max_images` summary values containing images. The images are built from `tensor` which must be 3-D with shape `[height, width, channels]` and where `channels` can be: * 1: `tensor` is interpreted as Grayscale. * 3: `tensor` is interpreted as RGB. * 4: `tensor` is interpreted as RGBA. The `name` in the outputted Summary.Value protobufs is generated based on the name, with a suffix depending on the max_outputs setting: * If `max_outputs` is 1, the summary value tag is '*name*/image'. * If `max_outputs` is greater than 1, the summary value tags are generated sequentially as '*name*/image/0', '*name*/image/1', etc. Args: tag: A name for the generated node. Will also serve as a series name in TensorBoard. tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width, channels]` where `channels` is 1, 3, or 4. 'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8). The image() function will scale the image values to [0, 255] by applying a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values will be clipped. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. )rescalerrir) r rrZrrSfloat32cliprW make_imagerr)rrXr dataformats scale_factorris&&&& rriri!sx6V_F F 0F%f-L ]]2:: &F# ) )!S 1 8 8 BF v /E '--C=> ??rc^\V4p\W4p\V4pVP\P4\ V4,p\ VP^^4P\P4VVVR7p\\P!WR7.R7#)z/Output a `Summary` protocol buffer with images.)rroislabelsrr) r rrrSrrZrrrWrr)r tensor_image tensor_boxesrrr#ris&&&&&& r image_boxesr&Fs<(L!, ??rc VP^,p\V4pVFAp\TW^3,W^3,W^3,W^3,VfRMW%,RR7pKC V#)rNRed)rnro)rranger) disp_imageboxesr# num_boxeslist_gtrs&&& r draw_boxesr.Wsj AIIG %  Q$K Q$K Q$K Q$K &FI  rc^RIHpVPwrVp\WQ,4p\Wa,4p VP V4p Ve\ WVR7p VP Pp V PW3V 4p ^RI p V P4p V PV RR7V P4pV P4\P!VVVVR7#)z=Convert a numpy representation of an image to Image protobuf.)ImageN)r#PNG)rheightr^ colorspaceencoded_image_string)rar0rr fromarrayr. ResamplingLANCZOSresizeioBytesIOsavegetvaluecloser)rXrr"r#r0r3r^channel scaled_height scaled_widthri ANTIALIASr:output image_strings&&&& rrrhs#\\F7()Mu'L OOF #E 5v6  ((I LL,6 BE ZZ\F JJveJ$??$L LLN ==)  rcF\V4p\V4p\V4pVP\P 4pW,P ^^4P\P4p\W4p\\P!WR7.R7#)rrr) r rrZrrSrrrW make_videorr)rrXfpsr videos&&& rrHrHsv V_F F #F%f-L ]]2:: &F# ) )!S 1 8 8 BF v #E '--C=> ??rc ^RIp^RIHp^RIpTP wrVrxTP \T4TR7p TPRR7;_uu_4p T Pp T PT R RR 7T P^4T P4p \P!YgYR 7uuRRR4# \d\R4R#i;i \d\RR4R#i;i \d>T PT R R R 7L \dT PT R R 7Li;ii;i +'giR#;i)rNzadd_video needs package moviepy)editorz6moviepy is installed, but can't import moviepy.editor.z2Some packages could be missing [imageio, requests])rGz.gif)suffixF)verboser)rL progress_bar)rLr2)moviepy ImportErrorprintrJtempfilerImageSequenceClipr*NamedTemporaryFiler write_gifrseekreadrr0) rXrGrNmpyrQ_thwcrrfilename tensor_strings && rrFrFs8),,KB1  f3 7D  $ $F $ 3 3q66 8 NN8U4N @ q  }}!  4 3%  /0  D @   " 8 8xUK 8x7 8 8 4 3 3spB;C E$C688E;CCC32C36 D>DED: 6D>7E9D: :D>>E E c\V4pVP4p\V4P4^8d\ R4VP R ^4pVP ^8wd \R4hV\P!\P4P,PR4p^RI p^RI pVP4pVPVR4;_uu_4pVP!^4VP#^4VP%V4VP'VP(4RRR4VP+4pVP-4\.P0!V^VP2R ,VRR7p \/\.P4!W R7.R 7# +'giL~;i) rQz4warning: audio amplitude out of range, auto clipped.z%input tensor should be 1 dimensional.zrAudiorr) rrXr`arrayr:rifio wave_write audio_stringres &&& rreresC FOE MMOE 5z~~! DE 2q! zzQDEE RXXbhh'++ + 3 3E :E  **,C 3  "" ,uzz*  <<>LIIK MMkk"o)  E '--C=> ??  s )AF.. F> c n.pVP4EFwr#.pVP4FwrVV^,pV^,R8Xd\V4^8wd \R4h\P!\PP V^,V^,V^,R7.R7p\P !WXR7p M.\P!VR7p \P !WZR7p VPV 4K VP\P!W$R74EK \P!VR 7p\P!R R 7p \V R 7p \R VP4.\4R7p \!\ P"!RWR7.R7#)rQMarginzlen(tags) != 3)rlowerupper)series)titler~r)rz multiline)rzchart)categorycustom_scalarsrr DT_STRINGrV string_valrcustom_scalars__config__rr)rrrr MarginChartContentSeriesChartMultilineChartContentrCategoryLayoutrrrrr rr)layout categoriesrrcharts chart_namechart_metadatatagsmgccr|mlccrrrXs& rr~r~skJ *+'') &J!!$Da H,t9>()9::!44"55<<"&q'aQ=#((zG!77DA"((zJ MM% !+4" *--ADE'*   3F!,,9IJK k 2C ,,./%'F  MM8 V  rc L\P!R\^R7P4R7p\VR7p\ RVP RR7.\ \ P!^R7.R7R 7p\\P!VR ,W4R 7.R 7#) rh)rrrrutf_8)encodingrrrz /text_summaryrr) rrr rrencoder rrr)rrhrrrXs&& rrhrhs!,,N1$=$O$O$QK k 2C KKK12%+;+?+?Q+G*HIF }}!6TU rc V^8d^p\P!WW4WV34p \^VR7P4p \P !RV R7p \ V R7p \ RV PR 4P4\\P!V P^,R7\P!V P^,R7.R7R7p \\P!W V R 7.R 7#) rnum_thresholds pr_curvesrrr<rrrVr=rrrr)rSstackr rrrrrr.r rrrr)rtpfptnfn precisionrecallrweightsropr_curve_plugin_datarrrXs&&&&&&&&& r pr_curve_rawr s 88RRY7 8D,.",,)=K k 2C ,,r"))+% $$$**Q-8 $$$**Q-8  F '--CfMN OOrc \V^4p\WW4R7p\^VR7P4p\P !RVR7p\ VR7p\ RVPR 4P4\\P!VP^,R7\P!VP^,R7.R7R 7p \\P!WV R 7.R 7#) r)rrrrrrr<rrrrrr)r compute_curver rrrrrr.r rrrr) rr# predictionsrrrorrrrXs &&&&& rpr_curver#s-N N D-.",,)=K k 2C ,,r"))+% $$$**Q-8 $$$**Q-8  F '--CfMN OOrcRpVfRp\P!\P!W^, ,44pVP\P4p^V^, 3p\P !VVVWc,R7wr\P !VVVRV, V,R7wr\P !VRRR1,4RRR1,p \P !V RRR1,4RRR1,p V ^,V , p V ^,V , pV \P!WKV ,4, pV \P!WKV,4, p\P!WWVV34#)gHz>Ng?)rr)rr) rSint32floorrfloat64rr maximumr)r#rrr_MINIMUM_COUNTbucket_indices float_labelshistogram_range tp_buckets_ fp_bucketsrrrrrrs&&&& rrr>s<NXXbhh{q6H'IJKN==,L.1,-OLL & MJLL |#w. MJ :dd# $TrT *B :dd# $TrT *B AB ABRZZR88I "**^"W5 5F 88RRY7 88rc &^RIp^RIHpVP!V4pVP VVVVVP VVR7p \ RVPR 4P4\\P!VP ^,R7\P!VP ^,R7\P!VP ^,R7.R7R7p\P!VPW4VV R7p V #) aCreate a tensor summary with summary metadata. Args: name: Uniquely identifiable name of the summary op. Could be replaced by combination of name and type to make it unique even outside of this summary. display_name: Will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. tensor: Tensor to display in summary. content_type: Type of content inside the Tensor. components: Bitmask representing present parts (vertices, colors, etc.) that belong to the summary. json_config: A string, JSON-serialized dictionary of ThreeJS classes configuration. Returns: Tensor summary with metadata. Nr) json_configr<rrrrr)r(tensorboard.plugins.meshr as_tensorcreate_summary_metadatarrrr.r rrrget_instance_name) r display_name descriptionrXrd componentsrr(rtensor_metadatatensor_summarys &&&&&&& r_get_tensor_summaryrcs.1 __V $F66  7O..$++-% $$&,,q/: $$&,,q/: $$&,,q/:  F]]  & &t : N rcBRpVe\P!VRR7pV#)z5Parse and returns JSON string from python dictionary.z{}T) sort_keys)jsondumps) config_dictrs& r_get_json_configrs#Kjj= rc ^RIHp^RIHp\ V4p .p WP 3W8P 3W(P3.p V U u.uFq^,fKV NK p p TPV U U u.uFwrV NK up p 4pV F%wrV P\VVVV V VV 44K' \V R7#uup iuup p i)aOutput a merged `Summary` protocol buffer with a mesh/point cloud. Args: tag: A name for this summary operation. vertices: Tensor of shape `[dim_1, ..., dim_n, 3]` representing the 3D coordinates of vertices. faces: Tensor of shape `[dim_1, ..., dim_n, 3]` containing indices of vertices within each triangle. colors: Tensor of shape `[dim_1, ..., dim_n, 3]` containing colors for each vertex. display_name: If set, will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. config_dict: Dictionary with ThreeJS classes names and configuration. Returns: Merged summary for mesh/point cloud representation. r)MeshPluginDatar) rr(tensorboard.plugins.mesh.plugin_data_pb2rrVERTEXFACECOLORget_components_bitmaskrrr)rverticescolorsfacesrrrrrr summariestensorsrXrdrs&&&&&&& rmeshrs,2G";/KI (() ##$ %%&G %,EG&ayvvGGE004; )r!r$rrrrrrir&r.rrHrFrer~rhrrrrr)blackrr)NNN)NFFr,)rQNCHW)rQCHWN)rQNN))iD)rN)NN)Wrloggingrtypingrr(rUrSgoogle.protobufr$tensorboard.compat.proto.summary_pb2rrr#tensorboard.compat.proto.tensor_pb2r)tensorboard.compat.proto.tensor_shape_pb2r !tensorboard.plugins.custom_scalarr ,tensorboard.plugins.pr_curve.plugin_data_pb2r (tensorboard.plugins.text.plugin_data_pb2r _convert_npr _utilsrr__all__ getLogger__name__rr!r$r0r5r8halffloat16bfloat16rrrdoubleint8rWqint8rhshortrrqint32int64 complex32chalf complex64cfloatr complex128cdoublequint8quint4x2rrZrrrrrrrrir&r.rrHrFrer~rhrrrrrrrrrrs&   ' <F8JC 2 0   8 $ &&:4  JJJ(;< MMIz+>? NN]J0CD MMJ _=   KK*k?;   MMK?  LL; o> JJI7 KK*i9 KK*i9 KK*i9 KK*i9 II I7 KK*i9 LL:y/:  KK*k?;!" OOnn6LM#$ KK..2HI OOnn6LM LL>>3IJ JJJ8 8NO MMO^5KL KK*lO< LL:|_= NNZ?5<5 (Vzz(MV"VJ@B@,5p"@J@""4@$ N@@#L P4P6"9J8v3$r