Operaciones de TensorFlow disponibles

En esta página, se describen las API de Python para TensorFlow y los operadores de grafos disponibles en Cloud TPU.

API de Python disponibles

La siguiente lista es una guía para el conjunto de las API de TensorFlow disponibles. Esta lista no es exhaustiva. Las funciones de biblioteca que no aparecen en esta lista pueden funcionar si constan de las funciones básicas disponibles.

Consulta la guía de rendimiento para obtener recomendaciones sobre los operadores específicos.

Módulo	API de Python disponible	Comentarios
tf	tf.abs
	tf.acosh
	tf.add
	tf.add_n
	tf.angle
	tf.arg_max	El argumento dimension debe ser una constante de tiempo de compilación.
	tf.arg_min	El argumento dimension debe ser una constante de tiempo de compilación.
	tf.asinh
	tf.assign	Disponible solo para la variable de recurso.
	tf.assign_add	Disponible solo para la variable de recurso.
	tf.assign_sub	Disponible solo para la variable de recurso.
	tf.atan
	tf.atan2
	tf.atanh
	tf.batch_to_space	Los argumentos crops y block_shape deben ser una constante de tiempo de compilación.
	tf.batch_to_space_nd	El argumento crops debe ser una constante de tiempo de compilación.
	tf.broadcast_dynamic_shape
	tf.broadcast_static_shape
	tf.case	Experimental (flujo de control). Es posible que aún no funcione de manera confiable.
	tf.cast
	tf.ceil
	tf.cholesky	Experimental. Puede tener problemas con la precisión numérica.
	tf.cholesky_solve	Experimental. Puede tener problemas con la precisión numérica.
	tf.clip_by_average_norm
	tf.clip_by_global_norm
	tf.clip_by_norm
	tf.clip_by_value
	tf.complex
	tf.concat	concat_dim debe ser una constante de tiempo de compilación.
	tf.cond	Experimental (flujo de control). Es posible que aún no funcione de manera confiable.
	tf.conj
	tf.constant
	tf.convert_to_tensor
	tf.cos
	tf.cosh
	tf.cross
	tf.cumprod	axis debe ser una constante de tiempo de compilación.
	tf.cumsum	axis debe ser una constante de tiempo de compilación.
	tf.depth_to_space
	tf.diag
	tf.diag_part
	tf.div	La división int32 es más lenta que otros tipos.
	tf.divide	La división int32 es más lenta que otros tipos.
	tf.dynamic_stitch	indices debe ser una constante de tiempo de compilación.
	tf.einsum
	tf.equal
	tf.erf
	tf.erfc
	tf.exp
	tf.expand_dims	dims debe ser una constante de tiempo de compilación.
	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	El argumento dims debe ser una constante de tiempo de compilación.
	tf.floor
	tf.floordiv
	tf.floormod
	tf.foldl	Experimental (flujo de control).
	tf.foldr	Experimental (flujo de control).
	tf.gather	axis debe ser una constante de tiempo de compilación.
	tf.gather_nd
	tf.greater
	tf.greater_equal
	tf.hessians	Experimental (flujo de control).
	tf.identity
	tf.identity_n
	tf.ifft
	tf.ifft2d
	tf.ifft3d
	tf.imag
	tf.invert_permutation	El argumento x debe ser una constante de tiempo de compilación.
	tf.is_finite
	tf.is_inf
	tf.is_nan
	tf.is_non_decreasing
	tf.is_strictly_increasing
	tf.less
	tf.less_equal
	tf.linspace	Los argumentos start, stop y num deben ser constantes de tiempo de compilación.
	tf.log
	tf.log1p
	tf.log_sigmoid
	tf.logical_and
	tf.logical_or
	tf.logical_not
	tf.logical_xor
	tf.matmul	Usa un matmul bfloat16 con acumulación float32.
	tf.matrix_band_part
	tf.matrix_diag
	tf.matrix_diag_part
	tf.matrix_set_diag
	tf.matrix_triangular_solve	Experimental. Puede tener problemas con la precisión numérica.
	tf.maximum
	tf.meshgrid
	tf.minimum
	tf.mod
	tf.multinomial	El argumento num_samples debe ser una constante de tiempo de compilación.
	tf.multiply
	tf.negative
	tf.no_op
	tf.norm
	tf.not_equal
	tf.one_hot	depth debe ser una constante de tiempo de compilación.
	tf.ones
	tf.ones_like
	tf.pad	El argumento paddings debe ser una constante de tiempo de compilación. La gradiente de relleno REFLECT aún no está disponible.
	tf.pow
	tf.random_normal	shape debe ser una constante de tiempo de compilación.
	tf.random_uniform	shape debe ser una constante de tiempo de compilación.
	tf.range	Los argumentos start, limit y delta deben ser constantes de tiempo de compilación.
	tf.rank
	tf.real
	tf.realdiv
	tf.reciprocal
	tf.reduce_all	axis debe ser una constante de tiempo de compilación.
	tf.reduce_any	axis debe ser una constante de tiempo de compilación.
	tf.reduce_logsumexp
	tf.reduce_max	axis debe ser una constante de tiempo de compilación.
	tf.reduce_min	axis debe ser una constante de tiempo de compilación.
	tf.reduce_prod	axis debe ser una constante de tiempo de compilación.
	tf.reduce_sum	axis debe ser una constante de tiempo de compilación.
	tf.reshape	El argumento shape debe ser una constante de tiempo de compilación.
	tf.reverse	El argumento dims debe ser una constante de tiempo de compilación.
	tf.reverse_sequence
	tf.reverse_v2	El argumento axis debe ser una constante de tiempo de compilación.
	tf.rint
	tf.round
	tf.rsqrt
	tf.saturate_cast
	tf.scalar_mul
	tf.scan	Experimental (flujo de control).
	tf.scatter_nd
	tf.sequence_mask
	tf.shape
	tf.shape_n
	tf.sigmoid
	tf.sign
	tf.sin
	tf.sinh
	tf.size
	tf.slice	size debe ser una constante de tiempo de compilación. Además, begin debe ser una constante de tiempo de compilación, o bien size no debe ser negativo. La propagación inversa solo se admite si begin y size son constantes de tiempo de compilación.
	tf.space_to_batch	paddings y block_shape deben ser constantes de tiempo de compilación.
	tf.space_to_batch_nd	paddings debe ser una constante de tiempo de compilación.
	tf.space_to_depth
	tf.split	axis debe ser una constante de tiempo de compilación.
	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	El argumento multiples debe ser una constante de tiempo de compilación.
	tf.to_bfloat16
	tf.to_float
	tf.to_int32
	tf.to_int64	La compatibilidad de int64 es limitada.
	tf.trace
	tf.transpose	El argumento perm debe ser una constante de tiempo de compilación.
	tf.truediv
	tf.truncated_normal	shape debe ser una constante de tiempo de compilación.
	tf.truncatediv
	tf.truncatemod
	tf.unsorted_segment_sum
	tf.unstack
	tf.where	Tanto x como y no pueden ser None. Si tanto x como y son None, el operador no tendría una forma estática.
	tf.while_loop	Para calcular el gradiente de un bucle mientras, se requiere el argumento maximum_iterations.
	tf.zeros
	tf.zeros_like
	tf.Tensor.__getitem__	El inicio, el final y las segmentaciones de un segmento deben ser constantes de tiempo de compilación.
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	El factor de recorte debe ser una constante de tiempo de compilación.
	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	Solo align_corners=True está disponible. size debe ser una constante de tiempo de compilación.
	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	Experimental.
	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	Experimental.
	tf.nn.static_rnn	Experimental.
	tf.nn.weighted_cross_entropy_with_logits	Experimental.
	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 debe ser una constante de tiempo de compilación.
	tf.spectral.irfft2d	fft_length debe ser una constante de tiempo de compilación.
	tf.spectral.irfft3d	fft_length debe ser una constante de tiempo de compilación.
	tf.spectral.rfft	fft_length debe ser una constante de tiempo de compilación.
	tf.spectral.rfft2d	fft_length debe ser una constante de tiempo de compilación.
	tf.spectral.rfft3d	fft_length debe ser una constante de tiempo de compilación.

API de Python no disponibles

Esta lista no es exhaustiva. A continuación, se muestran las opciones que no están disponibles en Cloud TPU:

Módulo	API de Python no disponible	Comentarios
tf	tf.accumulate_n	Usa las variables de referencia.
	tf.acos
	tf.asin
	tf.betainc
	tf.bitcast
	tf.add_check_numerics_ops	Los programas que contienen operadores numéricos de verificación se deben ejecutar; sin embargo, este operador se ignora actualmente.
	tf.assert_...	Los programas que contienen confirmaciones se deben ejecutar; sin embargo, las confirmaciones se ignoran.
	tf.check_numerics	Los programas que contienen operadores numéricos de verificación se deben ejecutar; sin embargo, este operador se ignora actualmente.
	tf.confusion_matrix
	tf.count_nonzero	Usa la reducción de 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

Operadores de grafo disponibles

Operador	Restricción de tipo
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}