TensorFlow Ops yang Tersedia

Halaman ini berisi daftar TensorFlow Python API dan operator grafik yang tersedia di Cloud TPU.

Python API yang tersedia

Daftar di bawah ini adalah panduan untuk kumpulan TensorFlow Python API yang tersedia. Ini bukan merupakan daftar lengkap. Fungsi library yang tidak ada dalam daftar ini dapat berfungsi jika terdiri dari primitif yang tersedia.

Lihat panduan performa untuk mendapatkan rekomendasi tentang operator tertentu.

Modul	API Python yang tersedia	Komentar
`tf`	`tf.abs`
	`tf.acosh`
	`tf.add`
	`tf.add_n`
	`tf.angle`
	`tf.arg_max`	Argumen `dimension` harus berupa konstanta waktu kompilasi.
	`tf.arg_min`	Argumen `dimension` harus berupa konstanta waktu kompilasi.
	`tf.asinh`
	`tf.assign`	Hanya tersedia untuk variabel resource.
	`tf.assign_add`	Hanya tersedia untuk variabel resource.
	`tf.assign_sub`	Hanya tersedia untuk variabel resource.
	`tf.atan`
	`tf.atan2`
	`tf.atanh`
	`tf.batch_to_space`	Argumen `crops` dan `block_shape` harus berupa konstanta waktu kompilasi.
	`tf.batch_to_space_nd`	Argumen `crops` harus berupa konstanta waktu kompilasi.
	`tf.broadcast_dynamic_shape`
	`tf.broadcast_static_shape`
	`tf.case`	Eksperimental (alur kontrol). Mungkin belum berfungsi dengan baik.
	`tf.cast`
	`tf.ceil`
	`tf.cholesky`	Eksperimental. Mungkin mengalami masalah dengan presisi numerik.
	`tf.cholesky_solve`	Eksperimental. Mungkin mengalami masalah dengan presisi numerik.
	`tf.clip_by_average_norm`
	`tf.clip_by_global_norm`
	`tf.clip_by_norm`
	`tf.clip_by_value`
	`tf.complex`
	`tf.concat`	`concat_dim` harus berupa konstanta waktu kompilasi.
	`tf.cond`	Eksperimental (alur kontrol). Mungkin belum berfungsi dengan baik.
	`tf.conj`
	`tf.constant`
	`tf.convert_to_tensor`
	`tf.cos`
	`tf.cosh`
	`tf.cross`
	`tf.cumprod`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.cumsum`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.depth_to_space`
	`tf.diag`
	`tf.diag_part`
	`tf.div`	Pembagian `int32` lebih lambat dari jenis lain.
	`tf.divide`	Pembagian `int32` lebih lambat dari jenis lain.
	`tf.dynamic_stitch`	`indices` harus berupa konstanta waktu kompilasi.
	`tf.einsum`
	`tf.equal`
	`tf.erf`
	`tf.erfc`
	`tf.exp`
	`tf.expand_dims`	`dims` harus berupa konstanta waktu kompilasi.
	`tf.expm1`
	`tf.extract_image_patches`
	`tf.eye`
	`tf.fake_quant_with_min_max_args`
	`tf.fake_quant_with_min_max_args_gradient`
	`tf.fake_quant_with_min_max_vars`
	`tf.fake_quant_with_min_max_vars_gradient`
	`tf.fft`
	`tf.fft2d`
	`tf.fft3d`
	`tf.fill`	Argumen `dims` harus berupa konstanta waktu kompilasi.
	`tf.floor`
	`tf.floordiv`
	`tf.floormod`
	`tf.foldl`	Eksperimental (alur kontrol).
	`tf.foldr`	Eksperimental (alur kontrol).
	`tf.gather`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.gather_nd`
	`tf.greater`
	`tf.greater_equal`
	`tf.hessians`	Eksperimental (control-flow.
	`tf.identity`
	`tf.identity_n`
	`tf.ifft`
	`tf.ifft2d`
	`tf.ifft3d`
	`tf.imag`
	`tf.invert_permutation`	Argumen `x` harus berupa konstanta waktu kompilasi.
	`tf.is_finite`
	`tf.is_inf`
	`tf.is_nan`
	`tf.is_non_decreasing`
	`tf.is_strictly_increasing`
	`tf.less`
	`tf.less_equal`
	`tf.linspace`	Argumen `start`, `stop`, dan `num` harus berupa konstanta waktu kompilasi.
	`tf.log`
	`tf.log1p`
	`tf.log_sigmoid`
	`tf.logical_and`
	`tf.logical_or`
	`tf.logical_not`
	`tf.logical_xor`
	`tf.matmul`	Menggunakan matmul `bfloat16` dengan akumulasi `float32`.
	`tf.matrix_band_part`
	`tf.matrix_diag`
	`tf.matrix_diag_part`
	`tf.matrix_set_diag`
	`tf.matrix_triangular_solve`	Eksperimental. Mungkin mengalami masalah dengan presisi numerik.
	`tf.maximum`
	`tf.meshgrid`
	`tf.minimum`
	`tf.mod`
	`tf.multinomial`	Argumen `num_samples` harus berupa konstanta waktu kompilasi.
	`tf.multiply`
	`tf.negative`
	`tf.no_op`
	`tf.norm`
	`tf.not_equal`
	`tf.one_hot`	`depth` harus berupa konstanta waktu kompilasi.
	`tf.ones`
	`tf.ones_like`
	`tf.pad`	Argumen `paddings` harus berupa konstanta waktu kompilasi. Gradien padding `REFLECT` belum tersedia.
	`tf.pow`
	`tf.random_normal`	`shape` harus berupa konstanta waktu kompilasi.
	`tf.random_uniform`	`shape` harus berupa konstanta waktu kompilasi.
	`tf.range`	Argumen `start`, `limit`, dan `delta` harus berupa konstanta waktu kompilasi.
	`tf.rank`
	`tf.real`
	`tf.realdiv`
	`tf.reciprocal`
	`tf.reduce_all`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reduce_any`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reduce_logsumexp`
	`tf.reduce_max`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reduce_min`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reduce_prod`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reduce_sum`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.reshape`	Argumen `shape` harus berupa konstanta waktu kompilasi.
	`tf.reverse`	Argumen `dims` harus berupa konstanta waktu kompilasi.
	`tf.reverse_sequence`
	`tf.reverse_v2`	Argumen `axis` harus berupa konstanta waktu kompilasi.
	`tf.rint`
	`tf.round`
	`tf.rsqrt`
	`tf.saturate_cast`
	`tf.scalar_mul`
	`tf.scan`	Eksperimental (alur kontrol).
	`tf.scatter_nd`
	`tf.sequence_mask`
	`tf.shape`
	`tf.shape_n`
	`tf.sigmoid`
	`tf.sign`
	`tf.sin`
	`tf.sinh`
	`tf.size`
	`tf.slice`	`size` harus berupa konstanta waktu kompilasi. Selain itu, `begin` harus berupa konstanta waktu kompilasi atau `size` harus non-negatif. Propagasi mundur hanya didukung jika `begin` dan `size` adalah konstanta waktu kompilasi.
	`tf.space_to_batch`	`paddings` dan `block_shape` harus berupa konstanta waktu kompilasi.
	`tf.space_to_batch_nd`	`paddings` harus berupa konstanta waktu kompilasi.
	`tf.space_to_depth`
	`tf.split`	`axis` harus berupa konstanta waktu kompilasi.
	`tf.sqrt`
	`tf.square`
	`tf.squared_difference`
	`tf.squeeze`
	`tf.stack`
	`tf.stop_gradient`
	`tf.strided_slice`
	`tf.tan`
	`tf.tanh`
	`tf.tensordot`
	`tf.tile`	Argumen `multiples` harus berupa konstanta waktu kompilasi.
	`tf.to_bfloat16`
	`tf.to_float`
	`tf.to_int32`
	`tf.to_int64`	Dukungan `int64` dibatasi.
	`tf.trace`
	`tf.transpose`	Argumen `perm` harus berupa konstanta waktu kompilasi.
	`tf.truediv`
	`tf.truncated_normal`	`shape` harus berupa konstanta waktu kompilasi.
	`tf.truncatediv`
	`tf.truncatemod`
	`tf.unsorted_segment_sum`
	`tf.unstack`
	`tf.where`	`x` dan `y` tidak boleh berupa `None`. Jika `x` dan `y` adalah `None`, operator tidak akan memiliki bentuk statis.
	`tf.while_loop`	Menghitung gradien loop sementara mengharuskan argumen `maximum_iterations` diteruskan.
	`tf.zeros`
	`tf.zeros_like`
	`tf.Tensor.__getitem__`	Awal, akhir, dan langkah slice harus berupa konstanta waktu kompilasi.
`tf.bitwise`	`tf.bitwise_and`
	`tf.bitwise_or`
	`tf.bitwise_invert`
`tf.contrib.stateless`	`tf.contrib.stateless.stateless_random_normal`
	`tf.contrib.stateless.stateless_random_uniform`
`tf.image`	`tf.image.adjust_brightness`
	`tf.image.adjust_contrast`
	`tf.image.adjust_gamma`
	`tf.image.adjust_hue`
	`tf.image.adjust_saturation`
	`tf.image.central_crop`	Faktor pangkas harus berupa konstanta waktu kompilasi.
	`tf.image.convert_image_dtype`
	`tf.image.flip_left_right`
	`tf.image.flip_up_down`
	`tf.image.grayscale_to_rgb`
	`tf.image.hsv_to_rgb`
	`tf.image.resize_bilinear`	Hanya tersedia `align_corners=True`. `size` harus berupa konstanta waktu kompilasi.
	`tf.image.random_brightness`
	`tf.image.random_contrast`
	`tf.image.random_flip_left_right`
	`tf.image.random_flip_up_down`
	`tf.image.random_hue`
	`tf.image.random_saturation`
	`tf.image.rgb_to_hsv`
	`tf.image.rgb_to_grayscale`
	`tf.image.rot90`
	`tf.image.total_variation`
	`tf.image.transpose_image`
`tf.layers`	`tf.layers.average_pooling1d`
	`tf.layers.average_pooling2d`
	`tf.layers.average_pooling1d`
	`tf.layers.batch_normalization`
	`tf.layers.conv1d`
	`tf.layers.conv2d`
	`tf.layers.conv2d_transpose`
	`tf.layers.conv3d`
	`tf.layers.conv3d_transpose`
	`tf.layers.dense`
	`tf.layers.dropout`
	`tf.layers.flatten`
	`tf.layers.max_pooling1d`
	`tf.layers.max_pooling2d`
	`tf.layers.max_pooling3d`
	`tf.layers.separable_conv2d`
`tf.nn`	`tf.nn.atrous_conv2d`
	`tf.nn.atrous_conv2d_transpose`
	`tf.nn.avg_pool`
	`tf.nn.avg_pool3d`
	`tf.nn.batch_normalization`
	`tf.nn.bias_add`
	`tf.nn.conv1d`
	`tf.nn.conv2d`
	`tf.nn.conv2d_backprop_filter`
	`tf.nn.conv2d_backprop_input`
	`tf.nn.conv2d_transpose`
	`tf.nn.conv3d`
	`tf.nn.conv3d_backprop_filter`
	`tf.nn.conv3d_backprop_input`
	`tf.nn.conv3d_transpose`
	`tf.nn.convolution`
	`tf.nn.crelu`
	`tf.nn.depthwise_conv2d`
	`tf.nn.depthwise_conv2d_native`
	`tf.nn.depthwise_conv2d_native_backprop_filter`
	`tf.nn.depthwise_conv2d_native_backprop_input`
	`tf.nn.dropout`
	`tf.nn.dynamic_rnn`	Eksperimental.
	`tf.nn.elu`
	`tf.nn.fused_batch_norm`
	`tf.nn.l2_loss`
	`tf.nn.l2_normalize`
	`tf.nn.leaky_relu`
	`tf.nn.local_response_normalization`
	`tf.nn.log_poisson_loss`
	`tf.nn.log_softmax`
	`tf.nn.max_pool`
	`tf.nn.max_pool3d`
	`tf.nn.moments`
	`tf.nn.normalize_moments`
	`tf.nn.pool`
	`tf.nn.relu`
	`tf.nn.relu6`
	`tf.nn.relu_layer`
	`tf.nn.selu`
	`tf.nn.separable_conv2d`
	`tf.nn.sigmoid_cross_entropy_with_logits`
	`tf.nn.softmax`
	`tf.nn.softmax_cross_entropy_with_logits`
	`tf.nn.softplus`
	`tf.nn.softsign`
	`tf.nn.sparse_softmax_cross_entropy_with_logits`
	`tf.nn.static_bidirectional_rnn`	Eksperimental.
	`tf.nn.static_rnn`	Eksperimental.
	`tf.nn.weighted_cross_entropy_with_logits`	Eksperimental.
	`tf.nn.weighted_moments`
	`tf.nn.with_space_to_batch`
	`tf.nn.xw_plus_b`
	`tf.nn.zero_fraction`
`tf.spectral`	`tf.spectral.fft`
	`tf.spectral.fft2d`
	`tf.spectral.fft3d`
	`tf.spectral.ifft`
	`tf.spectral.ifft2d`
	`tf.spectral.ifft3d`
	`tf.spectral.irfft`	`fft_length` harus berupa konstanta waktu kompilasi.
	`tf.spectral.irfft2d`	`fft_length` harus berupa konstanta waktu kompilasi.
	`tf.spectral.irfft3d`	`fft_length` harus berupa konstanta waktu kompilasi.
	`tf.spectral.rfft`	`fft_length` harus berupa konstanta waktu kompilasi.
	`tf.spectral.rfft2d`	`fft_length` harus berupa konstanta waktu kompilasi.
	`tf.spectral.rfft3d`	`fft_length` harus berupa konstanta waktu kompilasi.

Python API yang tidak tersedia

Ini bukan merupakan daftar lengkap. Operasi yang tidak tersedia di Cloud TPU meliputi:

Modul	API Python tidak tersedia	Komentar
`tf`	`tf.accumulate_n`	Menggunakan variabel Referensi.
	`tf.acos`
	`tf.asin`
	`tf.betainc`
	`tf.bitcast`
	`tf.add_check_numerics_ops`	Program yang berisi operator pemeriksaan numerik harus berjalan, tetapi operator pemeriksaan numerik saat ini diabaikan.
	`tf.assert_...`	Program yang berisi pernyataan harus berjalan, tetapi pernyataan akan diabaikan.
	`tf.check_numerics`	Program yang berisi operator pemeriksaan numerik harus berjalan, tetapi operator pemeriksaan numerik saat ini diabaikan.
	`tf.confusion_matrix`
	`tf.count_nonzero`	Menggunakan pengurangan `int64`.
	`tf.count_up_to`
	`tf.create_partitioned_variables`
	`tf.dequantize`
	`tf.digamma`
	`tf.dynamic_partition`
	`tf.edit_distance`
	`tf.fake_quant_with_min_max_vars_per_channel`
	`tf.fake_quant_with_min_max_vars_per_channel_gradient`
	`tf.histogram_fixed_width`
	`tf.igamma`
	`tf.igammac`
	`tf.lbeta`
	`tf.lgamma`
	`tf.matrix_determinant`
	`tf.matrix_inverse`
	`tf.matrix_solve`
	`tf.matrix_solve_ls`
	`tf.polygamma`
	`tf.py_func`
	`tf.qr`
	`tf.quantize_v2`
	`tf.quantized_concat`
	`tf.random_crop`
	`tf.random_gamma`
	`tf.random_poisson`
	`tf.random_shuffle`
	`tf.scatter_add`
	`tf.scatter_div`
	`tf.scatter_mul`
	`tf.scatter_nd_add`
	`tf.scatter_nd_sub`
	`tf.scatter_nd_update`
	`tf.segment_mean`
	`tf.segment_max`
	`tf.segment_min`
	`tf.segment_prod`
	`tf.segment_sum`
	`tf.self_adjoint_eig`
	`tf.self_adjoint_eigvals`
	`tf.setdiff1d`
	`tf.sparse_...`
	`tf.string_...`
	`tf.substr`
	`tf.svd`
	`tf.to_double`
	`tf.unique`
	`tf.unsorted_segment_max`
	`tf.zeta`
	`tf.bitwise.bitwise_xor`
	`tf.contrib.stateless.stateless_truncated_normal`

Operator grafik yang tersedia

Operator	Batasan Jenis
`Abs`	`T={bfloat16,float,int32,int64}`
`Acos`	`T={bfloat16,complex64,float,int32,int64}`
`Acosh`	`T={bfloat16,complex64,float}`
`Add`	`T={bfloat16,complex64,float,int32,int64}`
`AddN`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AdjustContrastv2`	`T={float}`
`AdjustHue`	`T={float}`
`AdjustSaturation`	`T={float}`
`All`	`Tidx={int32,int64}`
`AllToAll`	`T={bfloat16,float}`
`Angle`	`Tout={float}` `T={complex64}`
`Any`	`Tidx={int32,int64}`
`ApproximateEqual`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ArgMax`	`Tidx={int32,int64}` `output_type={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ArgMin`	`Tidx={int32,int64}` `output_type={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Asin`	`T={bfloat16,complex64,float,int32,int64}`
`Asinh`	`T={bfloat16,complex64,float}`
`Assert`	`T={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64}`
`AssignAddVariableOp`	`dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AssignSubVariableOp`	`dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AssignVariableOp`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Atan`	`T={bfloat16,complex64,float,int32,int64}`
`Atan2`	`T={bfloat16,float}`
`Atanh`	`T={bfloat16,complex64,float}`
`AvgPool`	`T={bfloat16,float}`
`AvgPool3D`	`T={bfloat16,float}`
`AvgPool3DGrad`	`T={bfloat16,float}`
`AvgPoolGrad`	`T={bfloat16,float}`
`BatchMatMul`	`T={bfloat16,complex64,float,int32,int64}`
`BatchToSpace`	`Tidx={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`BatchToSpaceND`	`Tcrops={int32,int64}` `Tblock_shape={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`BiasAdd`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BiasAddGrad`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BiasAddV1`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Bitcast`	`type={bfloat16,complex64,float,int32,int64,uint32,uint64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BitwiseAnd`	`T={int32,int64,uint32,uint64}`
`BitwiseOr`	`T={int32,int64,uint32,uint64}`
`BitwiseXor`	`T={int32,int64,uint32,uint64}`
`BroadcastArgs`	`T={int32,int64}`
`BroadcastGradientArgs`	`T={int32,int64}`
`BroadcastTo`	`Tidx={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Bucketize`	`T={float,int32,int64}`
`Cast`	`DstT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `SrcT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Ceil`	`T={bfloat16,float}`
`CheckNumerics`	`T={bfloat16,float}`
`Cholesky`	`T={float}`
`ClipByValue`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`CollectivePermute`	`T={bfloat16,float}`
`Complex`	`Tout={complex64}` `T={float}`
`ComplexAbs`	`Tout={float}` `T={complex64}`
`Concat`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ConcatOffset`
`ConcatV2`	`Tidx={int32}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Conj`	`T={complex64}`
`ConjugateTranspose`	`Tperm={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Const`	`dtype={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64}`
`ControlTrigger`
`Conv2D`	`T={bfloat16,float}`
`Conv2DBackpropFilter`	`T={bfloat16,float}`
`Conv2DBackpropInput`	`T={bfloat16,float}`
`Conv3D`	`T={bfloat16,float}`
`Conv3DBackpropFilterV2`	`T={bfloat16,float}`
`Conv3DBackpropInputV2`	`Tshape={int32,int64}` `T={bfloat16,float}`
`Cos`	`T={bfloat16,complex64,float}`
`Cosh`	`T={bfloat16,complex64,float}`
`Cross`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`CrossReplicaSum`	`T={bfloat16,float}`
`Cumprod`	`Tidx={int32,int64}` `T={bfloat16,float,int32}`
`Cumsum`	`Tidx={int32,int64}` `T={bfloat16,float,int32}`
`DataFormatVecPermute`	`T={int32,int64}`
`DepthToSpace`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`DepthwiseConv2dNative`	`T={bfloat16,float}`
`DepthwiseConv2dNativeBackpropFilter`	`T={bfloat16,float}`
`DepthwiseConv2dNativeBackpropInput`	`T={bfloat16,float}`
`Diag`	`T={bfloat16,complex64,float,int32,int64}`
`DiagPart`	`T={bfloat16,complex64,float,int32,int64}`
`Digamma`	`T={bfloat16,float}`
`Div`	`T={bfloat16,complex64,float,int32,int64}`
`DivNoNan`	`T={float}`
`DynamicStitch`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Elu`	`T={bfloat16,float}`
`EluGrad`	`T={bfloat16,float}`
`Empty`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`EmptyTensorList`	`shape_type={int32,int64}` `element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Equal`	`T={bfloat16,bool,complex64,float,int32,int64}`
`Erf`	`T={bfloat16,float}`
`Erfc`	`T={bfloat16,float}`
`Exp`	`T={bfloat16,complex64,float}`
`ExpandDims`	`Tdim={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Expm1`	`T={bfloat16,complex64,float}`
`ExtractImagePatches`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`FFT`	`Tcomplex={complex64}`
`FFT2D`	`Tcomplex={complex64}`
`FFT3D`	`Tcomplex={complex64}`
`FakeParam`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`FakeQuantWithMinMaxArgs`
`FakeQuantWithMinMaxArgsGradient`
`FakeQuantWithMinMaxVars`
`FakeQuantWithMinMaxVarsGradient`
`Fill`	`index_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Floor`	`T={bfloat16,float}`
`FloorDiv`	`T={bfloat16,complex64,float,int32,int64}`
`FloorMod`	`T={bfloat16,float,int32,int64}`
`FusedBatchNorm`	`T={float}`
`FusedBatchNormGrad`	`T={float}`
`FusedBatchNormGradV2`	`U={float}` `T={bfloat16,float}`
`FusedBatchNormV2`	`U={float}` `T={bfloat16,float}`
`Gather`	`Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GatherNd`	`Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GatherV2`	`Taxis={int32,int64}` `Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GetItem`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Greater`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`GreaterEqual`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`HSVToRGB`	`T={bfloat16,float}`
`IFFT`	`Tcomplex={complex64}`
`IFFT2D`	`Tcomplex={complex64}`
`IFFT3D`	`Tcomplex={complex64}`
`IRFFT`
`IRFFT2D`
`IRFFT3D`
`Identity`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`IdentityN`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`If`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Imag`	`Tout={float}` `T={complex64}`
`InfeedDequeue`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InfeedDequeueTuple`	`dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InplaceAdd`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InplaceUpdate`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Inv`	`T={bfloat16,complex64,float,int32,int64}`
`Invert`	`T={int32,int64,uint32,uint64}`
`InvertPermutation`	`T={int32}`
`IsFinite`	`T={bfloat16,float}`
`IsInf`	`T={bfloat16,float}`
`IsNan`	`T={bfloat16,float}`
`L2Loss`	`T={bfloat16,float}`
`LRN`	`T={bfloat16,float}`
`LRNGrad`	`T={bfloat16,float}`
`LeakyRelu`	`T={bfloat16,float}`
`LeakyReluGrad`	`T={bfloat16,float}`
`LeftShift`	`T={int32,int64,uint32,uint64}`
`Less`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`LessEqual`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Lgamma`	`T={bfloat16,float}`
`LinSpace`	`Tidx={int32,int64}` `T={bfloat16,float}`
`ListDiff`	`out_idx={int32,int64}` `T={int32,int64}`
`Log`	`T={bfloat16,complex64,float}`
`Log1p`	`T={bfloat16,complex64,float}`
`LogSoftmax`	`T={bfloat16,float}`
`LogicalAnd`
`LogicalNot`
`LogicalOr`
`MatMul`	`T={bfloat16,complex64,float}`
`MatrixBandPart`	`Tindex={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixDiag`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixDiagPart`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixSetDiag`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixTriangularSolve`	`T={complex64,float}`
`Max`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`MaxPool`	`T={bfloat16,float,int32,int64}`
`MaxPool3D`	`T={bfloat16,float}`
`MaxPool3DGrad`	`TInput={bfloat16,float}` `T={bfloat16,float}`
`MaxPool3DGradGrad`	`T={float}`
`MaxPoolGrad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`MaxPoolGradGrad`	`T={float}`
`MaxPoolGradGradV2`	`T={float}`
`MaxPoolGradV2`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`MaxPoolV2`	`T={bfloat16,float,int32,int64}`
`Maximum`	`T={bfloat16,float,int32,int64}`
`Mean`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Min`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Minimum`	`T={bfloat16,float,int32,int64}`
`MirrorPad`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Mod`	`T={bfloat16,float,int32,int64}`
`Mul`	`T={bfloat16,complex64,float,int32,int64}`
`Multinomial`	`output_dtype={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`Neg`	`T={bfloat16,complex64,float,int32,int64}`
`NoOp`
`NonMaxSuppressionV4`	`T={float}`
`NotEqual`	`T={bfloat16,bool,complex64,float,int32,int64}`
`OneHot`	`TI={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`OnesLike`	`T={bfloat16,bool,complex64,float,int32,int64}`
`OutfeedEnqueue`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`OutfeedEnqueueTuple`	`dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pack`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pad`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`PadV2`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ParallelDynamicStitch`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`PlaceholderWithDefault`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pow`	`T={bfloat16,complex64,float,int32,int64}`
`PreventGradient`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Prod`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Qr`	`T={float}`
`QuantizeAndDequantizeV2`	`T={bfloat16,float}`
`QuantizeAndDequantizeV3`	`T={bfloat16,float}`
`RFFT`
`RFFT2D`
`RFFT3D`
`RGBToHSV`	`T={bfloat16,float}`
`RandomShuffle`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`RandomStandardNormal`	`T={int32,int64}` `dtype={bfloat16,float}`
`RandomUniform`	`T={int32,int64}` `dtype={bfloat16,float}`
`RandomUniformInt`	`T={int32,int64}` `Tout={int32,int64}`
`Range`	`Tidx={bfloat16,float,int32,int64}`
`Rank`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ReadVariableOp`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Real`	`Tout={float}` `T={complex64}`
`RealDiv`	`T={bfloat16,complex64,float,int32,int64}`
`Reciprocal`	`T={bfloat16,complex64,float,int32,int64}`
`ReciprocalGrad`	`T={bfloat16,complex64,float}`
`RecvTPUEmbeddingActivations`
`Relu`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Relu6`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Relu6Grad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`ReluGrad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Reshape`	`Tshape={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResizeBilinear`	`T={bfloat16,float,int32,int64}`
`ResizeBilinearGrad`	`T={bfloat16,float}`
`ResizeNearestNeighbor`	`T={float,int32,int64}`
`ResourceApplyAdaMax`	`T={bfloat16,float}`
`ResourceApplyAdadelta`	`T={bfloat16,float}`
`ResourceApplyAdagrad`	`T={bfloat16,float}`
`ResourceApplyAdagradDA`	`T={bfloat16,float}`
`ResourceApplyAdam`	`T={bfloat16,float}`
`ResourceApplyAddSign`	`T={bfloat16,float}`
`ResourceApplyCenteredRMSProp`	`T={bfloat16,float}`
`ResourceApplyFtrl`	`T={bfloat16,float}`
`ResourceApplyFtrlV2`	`T={bfloat16,float}`
`ResourceApplyGradientDescent`	`T={bfloat16,float}`
`ResourceApplyKerasMomentum`	`T={bfloat16,float}`
`ResourceApplyMomentum`	`T={bfloat16,float}`
`ResourceApplyPowerSign`	`T={bfloat16,float}`
`ResourceApplyProximalAdagrad`	`T={bfloat16,float}`
`ResourceApplyProximalGradientDescent`	`T={bfloat16,float}`
`ResourceApplyRMSProp`	`T={bfloat16,float}`
`ResourceGather`	`Tindices={int32,int64}` `dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterAdd`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterDiv`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMax`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMin`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMul`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdAdd`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdSub`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdUpdate`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterSub`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterUpdate`	`Tindices={int32,int64}` `dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceStridedSliceAssign`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Reverse`	`T={bool,complex64,float,int32,int64}`
`ReverseSequence`	`Tlen={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ReverseV2`	`T={bfloat16,bool,complex64,float,int32,int64}` `Tidx={int32,int64}`
`RightShift`	`T={int32,int64,uint32,uint64}`
`Rint`	`T={bfloat16,float}`
`Round`	`T={bfloat16,complex64,float,int32,int64}`
`Rsqrt`	`T={bfloat16,complex64,float}`
`RsqrtGrad`	`T={bfloat16,complex64,float}`
`ScatterNd`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Select`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Selu`	`T={bfloat16,float}`
`SeluGrad`	`T={bfloat16,float}`
`SendTPUEmbeddingGradients`
`Shape`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ShapeN`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sigmoid`	`T={bfloat16,complex64,float}`
`SigmoidGrad`	`T={bfloat16,complex64,float}`
`Sign`	`T={bfloat16,complex64,float,int32,int64}`
`Sin`	`T={bfloat16,complex64,float}`
`Sinh`	`T={bfloat16,complex64,float}`
`Size`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Slice`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Snapshot`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Softmax`	`T={bfloat16,float}`
`SoftmaxCrossEntropyWithLogits`	`T={bfloat16,float}`
`Softplus`	`T={bfloat16,float}`
`SoftplusGrad`	`T={bfloat16,float}`
`Softsign`	`T={bfloat16,float}`
`SoftsignGrad`	`T={bfloat16,float}`
`SpaceToBatch`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SpaceToBatchND`	`Tblock_shape={int32,int64}` `Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SpaceToDepth`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SparseMatMul`	`Tb={bfloat16,float}` `Ta={bfloat16,float}`
`SparseSoftmaxCrossEntropyWithLogits`	`Tlabels={int32,int64}` `T={bfloat16,float}`
`SparseToDense`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Split`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SplitV`	`Tlen={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sqrt`	`T={bfloat16,complex64,float}`
`SqrtGrad`	`T={bfloat16,complex64,float}`
`Square`	`T={bfloat16,complex64,float,int32,int64}`
`SquaredDifference`	`T={bfloat16,complex64,float,int32,int64}`
`Squeeze`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackCloseV2`
`StackPopV2`	`elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackPushV2`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackV2`	`elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StatelessIf`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`StatelessMultinomial`	`output_dtype={int32,int64}` `Tseed={int32}` `T={bfloat16,float}`
`StatelessRandomNormal`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessRandomUniform`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessRandomUniformInt`	`Tseed={int32}` `T={int32,int64}` `dtype={int32,int64}`
`StatelessTruncatedNormal`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessWhile`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`StopGradient`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StridedSlice`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StridedSliceGrad`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sub`	`T={bfloat16,complex64,float,int32,int64}`
`Sum`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`SymbolicGradient`	`Tout={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TPUEmbeddingActivations`
`Tan`	`T={bfloat16,complex64,float,int32,int64}`
`Tanh`	`T={bfloat16,complex64,float}`
`TanhGrad`	`T={bfloat16,complex64,float}`
`TensorArrayCloseV3`
`TensorArrayConcatV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayGatherV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayGradV3`
`TensorArrayReadV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayScatterV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArraySizeV3`
`TensorArraySplitV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayWriteV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListElementShape`	`shape_type={int32,int64}`
`TensorListPopBack`	`element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListPushBack`	`element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListReserve`	`shape_type={int32,int64}` `element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Tile`	`Tmultiples={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TopKV2`	`T={bfloat16,float,int32,uint32}`
`Transpose`	`Tperm={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TruncateDiv`	`T={bfloat16,complex64,float,int32,int64}`
`TruncateMod`	`T={bfloat16,float,int32,int64}`
`TruncatedNormal`	`T={int32,int64}` `dtype={float}`
`Unpack`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`UnsortedSegmentMax`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`UnsortedSegmentMin`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`UnsortedSegmentProd`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`UnsortedSegmentSum`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`VarIsInitializedOp`
`VariableShape`	`out_type={int32,int64}`
`While`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Xdivy`	`T={complex64,float}`
`XlaBroadcastHelper`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaConv`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaDequantize`
`XlaDot`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaDynamicSlice`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaDynamicUpdateSlice`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaHostCompute`	`Toutputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tinputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaIf`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`XlaKeyValueSort`	`V={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `K={bfloat16,float,int32,int64,uint32,uint64}`
`XlaPad`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaRecv`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaRecvFromHost`	`Toutput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaReduce`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaReduceWindow`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaSelectAndScatter`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaSend`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaSendToHost`	`Tinput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaSort`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaWhile`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Xlogy`	`T={complex64,float}`
`ZerosLike`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_Arg`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`_ArrayToList`	`out_types={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_ListToArray`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_Retval`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`