Available TensorFlow Ops

This page lists the TensorFlow Python APIs and graph operators available on Cloud TPU.

Available Python APIs

The list below is a guide to the set of available TensorFlow Python APIs. This list is not exhaustive. Library functions not on this list may work if they are composed of available primitives.

See the performance guide for recommendations about specific operators.

Module Available Python API Comments
tf tf.abs
tf.acosh
tf.add
tf.add_n
tf.angle
tf.arg_max dimension argument must be a compile-time constant.
tf.arg_min dimension argument must be a compile-time constant.
tf.asinh
tf.assign Available only for resource variable.
tf.assign_add Available only for resource variable.
tf.assign_sub Available only for resource variable.
tf.atan
tf.atan2
tf.atanh
tf.batch_to_space crops and block_shape arguments must be compile-time constant.
tf.batch_to_space_nd crops argument must be a compile-time constant.
tf.broadcast_dynamic_shape
tf.broadcast_static_shape
tf.case Experimental (control-flow). May not work reliably yet.
tf.cast
tf.ceil
tf.cholesky Experimental. May have issues with numerical precision.
tf.cholesky_solve Experimental. May have issues with numerical precision.
tf.clip_by_average_norm
tf.clip_by_global_norm
tf.clip_by_norm
tf.clip_by_value
tf.complex
tf.concat concat_dim must be a compile-time constant.
tf.cond Experimental (control-flow). May not work reliably yet.
tf.conj
tf.constant
tf.convert_to_tensor
tf.cos
tf.cosh
tf.cross
tf.cumprod axis must be a compile-time constant.
tf.cumsum axis must be a compile-time constant.
tf.depth_to_space
tf.diag
tf.diag_part
tf.div int32 division is slower than other types.
tf.divide int32 division is slower than other types.
tf.dynamic_stitch indices must be a compile-time constant.
tf.einsum
tf.equal
tf.exp
tf.expand_dims dims must be a compile-time constant.
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 Experimental.
tf.fft2d Experimental.
tf.fft3d Experimental.
tf.fill dims argument must be a compile-time constant.
tf.floor
tf.floordiv
tf.floormod
tf.foldl Experimental (control-flow).
tf.foldr Experimental (control-flow).
tf.gather axis must be a compile-time constant.
tf.gather_nd
tf.greater
tf.greater_equal
tf.hessians Experimental (control-flow.
tf.identity
tf.identity_n
tf.ifft Experimental.
tf.ifft2d Experimental.
tf.ifft3d Experimental.
tf.imag
tf.invert_permutation x argument must be a compile-time constant.
tf.is_finite
tf.is_inf
tf.is_nan
tf.is_non_decreasing
tf.is_strictly_increasing
tf.less
tf.less_equal
tf.linspace start, stop and num arguments must be compile-time constants.
tf.log
tf.log1p
tf.log_sigmoid
tf.logical_and
tf.logical_or
tf.logical_not
tf.logical_xor
tf.matmul Uses a bfloat16 matmul with float32 accumulation.
tf.matrix_band_part
tf.matrix_diag
tf.matrix_diag_part
tf.matrix_set_diag
tf.matrix_triangular_solve Experimental. May have issues with numerical precision.
tf.maximum
tf.meshgrid
tf.minimum
tf.mod
tf.multinomial num_samples argument must be a compile-time constant.
tf.multiply
tf.negative
tf.no_op
tf.norm
tf.not_equal
tf.one_hot depth must be a compile-time constant.
tf.ones
tf.ones_like
tf.pad paddings argument must be a compile-time constant. Gradient of REFLECT padding is not yet available.
tf.pow
tf.random_normal shape must be a compile-time constant.
tf.random_uniform shape must be a compile-time constant.
tf.range start, limit and delta arguments must be compile-time constants.
tf.rank
tf.real
tf.realdiv
tf.reciprocal
tf.reduce_all axis must be a compile-time constant.
tf.reduce_any axis must be a compile-time constant.
tf.reduce_logsumexp
tf.reduce_max axis must be a compile-time constant.
tf.reduce_min axis must be a compile-time constant.
tf.reduce_prod axis must be a compile-time constant.
tf.reduce_sum axis must be a compile-time constant.
tf.reshape shape argument must be a compile-time constant.
tf.reverse dims argument must be a compile-time constant.
tf.reverse_sequence
tf.reverse_v2 axis argument must be a compile-time constant.
tf.rint
tf.round
tf.rsqrt
tf.saturate_cast
tf.scalar_mul
tf.scan Experimental (control-flow).
tf.scatter_nd
tf.sequence_mask
tf.shape
tf.shape_n
tf.sigmoid
tf.sign
tf.sin
tf.sinh
tf.size
tf.slice size must be a compile-time constant. In addition, either begin must be a compile-time constant or size must be non-negative. Backpropagation is only supported if begin and size are compile-time constants.
tf.space_to_batch paddings and block_shape must be compile-time constants.
tf.space_to_batch_nd paddings must be a compile-time constant.
tf.space_to_depth
tf.split axis must be a compile-time constant.
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 multiples argument must be a compile-time constant.
tf.to_bfloat16
tf.to_float
tf.to_int32
tf.to_int64 int64 support is limited.
tf.trace
tf.transpose perm argument must be a compile-time constant.
tf.truediv
tf.truncated_normal shape must be a compile-time constant.
tf.truncatediv
tf.truncatemod
tf.unsorted_segment_sum
tf.unstack
tf.where Both x and y must be non-None. If both x and y are None, the operator would not have a static shape.
tf.while_loop Computing the gradient of a while loop requires that the maximum_iterations argument is passed.
tf.zeros
tf.zeros_like
tf.Tensor.__getitem__ The start, end, and strides of a slice must be compile-time constants.
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 Crop factor must be a compile-time constant.
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 Only align_corners=True is available. size must be a compile-time constant.
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 Experimental.
tf.spectral.fft2d Experimental.
tf.spectral.fft3d Experimental.
tf.spectral.ifft Experimental.
tf.spectral.ifft2d Experimental.
tf.spectral.ifft3d Experimental.
tf.spectral.irfft Experimental. fft_length must be a compile-time constant.
tf.spectral.irfft2d Experimental. fft_length must be a compile-time constant.
tf.spectral.irfft3d Experimental. fft_length must be a compile-time constant.
tf.spectral.rfft Experimental. fft_length must be a compile-time constant.
tf.spectral.rfft2d Experimental. fft_length must be a compile-time constant.
tf.spectral.rfft3d Experimental. fft_length must be a compile-time constant.

Unavailable Python APIs

This list is not exhaustive. Ops that are not available on Cloud TPU include the following:

Module Unavailable Python API Comments
tf tf.accumulate_n Uses Ref variables.
tf.acos
tf.asin
tf.betainc
tf.bitcast
tf.add_check_numerics_ops Programs containing check numerics operators should run, but the check numerics operator is currently ignored.
tf.assert_... Programs containing assertions should run, but the assertions are ignored.
tf.check_numerics Programs containing check numerics operators should run, but the check numerics operator is currently ignored.
tf.confusion_matrix
tf.count_nonzero Uses int64 reduction.
tf.count_up_to
tf.create_partitioned_variables
tf.dequantize
tf.digamma
tf.dynamic_partition
tf.edit_distance
tf.erf
tf.erfc
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.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

Available graph operators

Operator Type Constraint
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
AdjustHue
AdjustSaturation
All Tidx={int32,int64}
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}
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}
T={bfloat16,complex64,float,int32,int64}
BitwiseAnd T={int32,int64,uint32,uint64}
BitwiseOr T={int32,int64,uint32,uint64}
BroadcastArgs T={int32,int64}
BroadcastGradientArgs T={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}
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}
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 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}
Cumsum Tidx={int32,int64}
T={bfloat16,float}
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}
Div T={bfloat16,complex64,float,int32,int64}
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}
Equal T={bfloat16,bool,complex64,float,int32,int64}
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
FFT2D
FFT3D
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}
Greater T={bfloat16,float,int32,int64,uint32,uint64}
GreaterEqual T={bfloat16,float,int32,int64,uint32,uint64}
HSVToRGB T={bfloat16,float}
IFFT
IFFT2D
IFFT3D
IRFFT
IRFFT2D
IRFFT3D
Identity T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}
IdentityN T={bfloat16,bool,complex64,float,int32,int64,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}
LeftShift T={int32,int64,uint32,uint64}
Less T={bfloat16,float,int32,int64,uint32,uint64}
LessEqual T={bfloat16,float,int32,int64,uint32,uint64}
LinSpace Tidx={int32,int64}
T={bfloat16,float}
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}
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
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}
QuantizeAndDequantizeV2 T={bfloat16,float}
RFFT
RFFT2D
RFFT3D
RGBToHSV T={bfloat16,float}
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}
ResourceApplyAdagrad T={bfloat16,float}
ResourceApplyAdam T={bfloat16,float}
ResourceApplyFtrl T={bfloat16,float}
ResourceApplyFtrlV2 T={bfloat16,float}
ResourceApplyGradientDescent T={bfloat16,float}
ResourceApplyMomentum T={bfloat16,float}
ResourceApplyRMSProp T={bfloat16,float}
ResourceGather Tindices={int32,int64}
dtype={bfloat16,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,int32,int64,uint32,uint64}
SoftplusGrad T={bfloat16,float,int32,int64,uint32,uint64}
Softsign T={bfloat16,float,int32,int64,uint32,uint64}
SoftsignGrad T={bfloat16,float,int32,int64,uint32,uint64}
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}
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}
StatelessRandomNormal Tseed={int32}
T={int32,int64}
dtype={float}
StatelessRandomUniform Tseed={int32}
T={int32,int64}
dtype={float}
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}
Tile Tmultiples={int32,int64}
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}
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={bfloat16,float}
Unpack T={bfloat16,bool,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}
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}
XlaRecv dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}
XlaReduceWindow T={bfloat16,complex64,float,int32,int64,uint32,uint64}
XlaSend T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}
XlaWhile T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}
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,uint32,uint64}
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