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Decorator that overrides the default implementation for a TensorFlow API.
tf.experimental.dispatch_for_api(
api, *signatures
)
The decorated function (known as the "dispatch target") will override the
default implementation for the API when the API is called with parameters that
match a specified type signature. Signatures are specified using dictionaries
that map parameter names to type annotations. E.g., in the following example,
masked_add
will be called for tf.add
if both x
and y
are
MaskedTensor
s:
class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
@dispatch_for_api(tf.math.add, {'x': MaskedTensor, 'y': MaskedTensor})
def masked_add(x, y, name=None):
return MaskedTensor(x.values + y.values, x.mask & y.mask)
mt = tf.add(MaskedTensor([1, 2], [True, False]), MaskedTensor(10, True))
print(f"values={mt.values.numpy()}, mask={mt.mask.numpy()}")
values=[11 12], mask=[ True False]
If multiple type signatures are specified, then the dispatch target will be
called if any of the signatures match. For example, the following code
registers masked_add
to be called if x
is a MaskedTensor
or y
is
a MaskedTensor
.
@dispatch_for_api(tf.math.add, {'x': MaskedTensor}, {'y':MaskedTensor})
def masked_add(x, y):
x_values = x.values if isinstance(x, MaskedTensor) else x
x_mask = x.mask if isinstance(x, MaskedTensor) else True
y_values = y.values if isinstance(y, MaskedTensor) else y
y_mask = y.mask if isinstance(y, MaskedTensor) else True
return MaskedTensor(x_values + y_values, x_mask & y_mask)
The type annotations in type signatures may be type objects (e.g.,
MaskedTensor
), typing.List
values, or typing.Union
values. For
example, the following will register masked_concat
to be called if values
is a list of MaskedTensor
values:
@dispatch_for_api(tf.concat, {'values': typing.List[MaskedTensor]})
def masked_concat(values, axis):
return MaskedTensor(tf.concat([v.values for v in values], axis),
tf.concat([v.mask for v in values], axis))
Each type signature must contain at least one subclass of tf.CompositeTensor
(which includes subclasses of tf.ExtensionType
), and dispatch will only be
triggered if at least one type-annotated parameter contains a
CompositeTensor
value. This rule avoids invoking dispatch in degenerate
cases, such as the following examples:
@dispatch_for_api(tf.concat, {'values': List[MaskedTensor]})
: Will not dispatch to the decorated dispatch target when the user callstf.concat([])
.@dispatch_for_api(tf.add, {'x': Union[MaskedTensor, Tensor], 'y': Union[MaskedTensor, Tensor]})
: Will not dispatch to the decorated dispatch target when the user callstf.add(tf.constant(1), tf.constant(2))
.
The dispatch target's signature must match the signature of the API that is
being overridden. In particular, parameters must have the same names, and
must occur in the same order. The dispatch target may optionally elide the
"name" parameter, in which case it will be wrapped with a call to
tf.name_scope
when appropraite.
Returns | |
---|---|
A decorator that overrides the default implementation for api .
|
Registered APIs
The TensorFlow APIs that may be overridden by @dispatch_for_api
are:
tf.__operators__.add(x, y, name)
tf.__operators__.eq(self, other)
tf.__operators__.getitem(tensor, slice_spec, var)
tf.__operators__.ne(self, other)
tf.__operators__.ragged_getitem(rt_input, key)
tf.approx_top_k(input, k, reduction_dimension, recall_target, is_max_k, reduction_input_size_override, aggregate_to_topk, name)
tf.argsort(values, axis, direction, stable, name)
tf.audio.decode_wav(contents, desired_channels, desired_samples, name)
tf.audio.encode_wav(audio, sample_rate, name)
tf.batch_to_space(input, block_shape, crops, name)
tf.bitcast(input, type, name)
tf.bitwise.bitwise_and(x, y, name)
tf.bitwise.bitwise_or(x, y, name)
tf.bitwise.bitwise_xor(x, y, name)
tf.bitwise.invert(x, name)
tf.bitwise.left_shift(x, y, name)
tf.bitwise.right_shift(x, y, name)
tf.boolean_mask(tensor, mask, axis, name)
tf.broadcast_dynamic_shape(shape_x, shape_y)
tf.broadcast_static_shape(shape_x, shape_y)
tf.broadcast_to(input, shape, name)
tf.case(pred_fn_pairs, default, exclusive, strict, name)
tf.cast(x, dtype, name)
tf.clip_by_global_norm(t_list, clip_norm, use_norm, name)
tf.clip_by_norm(t, clip_norm, axes, name)
tf.clip_by_value(t, clip_value_min, clip_value_max, name)
tf.compat.v1.Print(input_, data, message, first_n, summarize, name)
tf.compat.v1.arg_max(input, dimension, output_type, name)
tf.compat.v1.arg_min(input, dimension, output_type, name)
tf.compat.v1.batch_gather(params, indices, name)
tf.compat.v1.batch_to_space(input, crops, block_size, name, block_shape)
tf.compat.v1.batch_to_space_nd(input, block_shape, crops, name)
tf.compat.v1.boolean_mask(tensor, mask, name, axis)
tf.compat.v1.case(pred_fn_pairs, default, exclusive, strict, name)
tf.compat.v1.clip_by_average_norm(t, clip_norm, name)
tf.compat.v1.cond(pred, true_fn, false_fn, strict, name, fn1, fn2)
tf.compat.v1.convert_to_tensor(value, dtype, name, preferred_dtype, dtype_hint)
tf.compat.v1.debugging.assert_all_finite(t, msg, name, x, message)
tf.compat.v1.debugging.assert_equal(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_greater(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_greater_equal(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_integer(x, message, name)
tf.compat.v1.debugging.assert_less(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_less_equal(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_near(x, y, rtol, atol, data, summarize, message, name)
tf.compat.v1.debugging.assert_negative(x, data, summarize, message, name)
tf.compat.v1.debugging.assert_non_negative(x, data, summarize, message, name)
tf.compat.v1.debugging.assert_non_positive(x, data, summarize, message, name)
tf.compat.v1.debugging.assert_none_equal(x, y, data, summarize, message, name)
tf.compat.v1.debugging.assert_positive(x, data, summarize, message, name)
tf.compat.v1.debugging.assert_rank(x, rank, data, summarize, message, name)
tf.compat.v1.debugging.assert_rank_at_least(x, rank, data, summarize, message, name)
tf.compat.v1.debugging.assert_rank_in(x, ranks, data, summarize, message, name)
tf.compat.v1.debugging.assert_scalar(tensor, name, message)
tf.compat.v1.debugging.assert_shapes(shapes, data, summarize, message, name)
tf.compat.v1.debugging.assert_type(tensor, tf_type, message, name)
tf.compat.v1.decode_raw(input_bytes, out_type, little_endian, name, bytes)
tf.compat.v1.div(x, y, name)
tf.compat.v1.expand_dims(input, axis, name, dim)
tf.compat.v1.floor_div(x, y, name)
tf.compat.v1.foldl(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, name)
tf.compat.v1.foldr(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, name)
tf.compat.v1.gather(params, indices, validate_indices, name, axis, batch_dims)
tf.compat.v1.gather_nd(params, indices, name, batch_dims)
tf.compat.v1.image.crop_and_resize(image, boxes, box_ind, crop_size, method, extrapolation_value, name, box_indices)
tf.compat.v1.image.draw_bounding_boxes(images, boxes, name, colors)
tf.compat.v1.image.extract_glimpse(input, size, offsets, centered, normalized, uniform_noise, name)
tf.compat.v1.image.extract_image_patches(images, ksizes, strides, rates, padding, name, sizes)
tf.compat.v1.image.resize_area(images, size, align_corners, name)
tf.compat.v1.image.resize_bicubic(images, size, align_corners, name, half_pixel_centers)
tf.compat.v1.image.resize_bilinear(images, size, align_corners, name, half_pixel_centers)
tf.compat.v1.image.resize_image_with_pad(image, target_height, target_width, method, align_corners)
tf.compat.v1.image.resize_images(images, size, method, align_corners, preserve_aspect_ratio, name)
tf.compat.v1.image.resize_nearest_neighbor(images, size, align_corners, name, half_pixel_centers)
tf.compat.v1.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed, seed2, min_object_covered, aspect_ratio_range, area_range, max_attempts, use_image_if_no_bounding_boxes, name)
tf.compat.v1.io.decode_csv(records, record_defaults, field_delim, use_quote_delim, name, na_value, select_cols)
tf.compat.v1.io.parse_example(serialized, features, name, example_names)
tf.compat.v1.io.parse_single_example(serialized, features, name, example_names)
tf.compat.v1.io.serialize_many_sparse(sp_input, name, out_type)
tf.compat.v1.io.serialize_sparse(sp_input, name, out_type)
tf.compat.v1.losses.absolute_difference(labels, predictions, weights, scope, loss_collection, reduction)
tf.compat.v1.losses.compute_weighted_loss(losses, weights, scope, loss_collection, reduction)
tf.compat.v1.losses.cosine_distance(labels, predictions, axis, weights, scope, loss_collection, reduction, dim)
tf.compat.v1.losses.hinge_loss(labels, logits, weights, scope, loss_collection, reduction)
tf.compat.v1.losses.huber_loss(labels, predictions, weights, delta, scope, loss_collection, reduction)
tf.compat.v1.losses.log_loss(labels, predictions, weights, epsilon, scope, loss_collection, reduction)
tf.compat.v1.losses.mean_pairwise_squared_error(labels, predictions, weights, scope, loss_collection)
tf.compat.v1.losses.mean_squared_error(labels, predictions, weights, scope, loss_collection, reduction)
tf.compat.v1.losses.sigmoid_cross_entropy(multi_class_labels, logits, weights, label_smoothing, scope, loss_collection, reduction)
tf.compat.v1.losses.softmax_cross_entropy(onehot_labels, logits, weights, label_smoothing, scope, loss_collection, reduction)
tf.compat.v1.losses.sparse_softmax_cross_entropy(labels, logits, weights, scope, loss_collection, reduction)
tf.compat.v1.math.argmax(input, axis, name, dimension, output_type)
tf.compat.v1.math.argmin(input, axis, name, dimension, output_type)
tf.compat.v1.math.confusion_matrix(labels, predictions, num_classes, dtype, name, weights)
tf.compat.v1.math.count_nonzero(input_tensor, axis, keepdims, dtype, name, reduction_indices, keep_dims, input)
tf.compat.v1.math.in_top_k(predictions, targets, k, name)
tf.compat.v1.math.reduce_all(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_any(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_logsumexp(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_max(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_mean(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_min(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_prod(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.reduce_sum(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
tf.compat.v1.math.scalar_mul(scalar, x, name)
tf.compat.v1.nn.avg_pool(value, ksize, strides, padding, data_format, name, input)
tf.compat.v1.nn.batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name, input, mean, variance)
tf.compat.v1.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length, initial_state_fw, initial_state_bw, dtype, parallel_iterations, swap_memory, time_major, scope)
tf.compat.v1.nn.conv1d(value, filters, stride, padding, use_cudnn_on_gpu, data_format, name, input, dilations)
tf.compat.v1.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)
tf.compat.v1.nn.conv2d_backprop_filter(input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu, data_format, dilations, name)
tf.compat.v1.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)
tf.compat.v1.nn.conv2d_transpose(value, filter, output_shape, strides, padding, data_format, name, input, filters, dilations)
tf.compat.v1.nn.conv3d(input, filter, strides, padding, data_format, dilations, name, filters)
tf.compat.v1.nn.conv3d_backprop_filter(input, filter_sizes, out_backprop, strides, padding, data_format, dilations, name)
tf.compat.v1.nn.conv3d_transpose(value, filter, output_shape, strides, padding, data_format, name, input, filters, dilations)
tf.compat.v1.nn.convolution(input, filter, padding, strides, dilation_rate, name, data_format, filters, dilations)
tf.compat.v1.nn.crelu(features, name, axis)
tf.compat.v1.nn.ctc_beam_search_decoder(inputs, sequence_length, beam_width, top_paths, merge_repeated)
tf.compat.v1.nn.ctc_loss(labels, inputs, sequence_length, preprocess_collapse_repeated, ctc_merge_repeated, ignore_longer_outputs_than_inputs, time_major, logits)
tf.compat.v1.nn.ctc_loss_v2(labels, logits, label_length, logit_length, logits_time_major, unique, blank_index, name)
tf.compat.v1.nn.depth_to_space(input, block_size, name, data_format)
tf.compat.v1.nn.depthwise_conv2d(input, filter, strides, padding, rate, name, data_format, dilations)
tf.compat.v1.nn.depthwise_conv2d_native(input, filter, strides, padding, data_format, dilations, name)
tf.compat.v1.nn.dilation2d(input, filter, strides, rates, padding, name, filters, dilations)
tf.compat.v1.nn.dropout(x, keep_prob, noise_shape, seed, name, rate)
tf.compat.v1.nn.dynamic_rnn(cell, inputs, sequence_length, initial_state, dtype, parallel_iterations, swap_memory, time_major, scope)
tf.compat.v1.nn.embedding_lookup(params, ids, partition_strategy, name, validate_indices, max_norm)
tf.compat.v1.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, partition_strategy, name, combiner, max_norm)
tf.compat.v1.nn.erosion2d(value, kernel, strides, rates, padding, name)
tf.compat.v1.nn.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name)
tf.compat.v1.nn.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name)
tf.compat.v1.nn.fused_batch_norm(x, scale, offset, mean, variance, epsilon, data_format, is_training, name, exponential_avg_factor)
tf.compat.v1.nn.log_softmax(logits, axis, name, dim)
tf.compat.v1.nn.max_pool(value, ksize, strides, padding, data_format, name, input)
tf.compat.v1.nn.max_pool_with_argmax(input, ksize, strides, padding, data_format, Targmax, name, output_dtype, include_batch_in_index)
tf.compat.v1.nn.moments(x, axes, shift, name, keep_dims, keepdims)
tf.compat.v1.nn.nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name)
tf.compat.v1.nn.pool(input, window_shape, pooling_type, padding, dilation_rate, strides, name, data_format, dilations)
tf.compat.v1.nn.quantized_avg_pool(input, min_input, max_input, ksize, strides, padding, name)
tf.compat.v1.nn.quantized_conv2d(input, filter, min_input, max_input, min_filter, max_filter, strides, padding, out_type, dilations, name)
tf.compat.v1.nn.quantized_max_pool(input, min_input, max_input, ksize, strides, padding, name)
tf.compat.v1.nn.quantized_relu_x(features, max_value, min_features, max_features, out_type, name)
tf.compat.v1.nn.raw_rnn(cell, loop_fn, parallel_iterations, swap_memory, scope)
tf.compat.v1.nn.relu_layer(x, weights, biases, name)
tf.compat.v1.nn.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights, combiner, default_id, name, partition_strategy, max_norm)
tf.compat.v1.nn.sampled_softmax_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, partition_strategy, name, seed)
tf.compat.v1.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, rate, name, data_format, dilations)
tf.compat.v1.nn.sigmoid_cross_entropy_with_logits(labels, logits, name)
tf.compat.v1.nn.softmax(logits, axis, name, dim)
tf.compat.v1.nn.softmax_cross_entropy_with_logits(labels, logits, dim, name, axis)
tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2(labels, logits, axis, name, dim)
tf.compat.v1.nn.space_to_batch(input, paddings, block_size, name, block_shape)
tf.compat.v1.nn.space_to_depth(input, block_size, name, data_format)
tf.compat.v1.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name)
tf.compat.v1.nn.static_bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw, initial_state_bw, dtype, sequence_length, scope)
tf.compat.v1.nn.static_rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
tf.compat.v1.nn.static_state_saving_rnn(cell, inputs, state_saver, state_name, sequence_length, scope)
tf.compat.v1.nn.sufficient_statistics(x, axes, shift, keep_dims, name, keepdims)
tf.compat.v1.nn.weighted_cross_entropy_with_logits(labels, logits, pos_weight, name, targets)
tf.compat.v1.nn.weighted_moments(x, axes, frequency_weights, name, keep_dims, keepdims)
tf.compat.v1.nn.xw_plus_b(x, weights, biases, name)
tf.compat.v1.norm(tensor, ord, axis, keepdims, name, keep_dims)
tf.compat.v1.ones_like(tensor, dtype, name, optimize)
tf.compat.v1.pad(tensor, paddings, mode, name, constant_values)
tf.compat.v1.py_func(func, inp, Tout, stateful, name)
tf.compat.v1.quantize_v2(input, min_range, max_range, T, mode, name, round_mode, narrow_range, axis, ensure_minimum_range)
tf.compat.v1.ragged.constant_value(pylist, dtype, ragged_rank, inner_shape, row_splits_dtype)
tf.compat.v1.ragged.placeholder(dtype, ragged_rank, value_shape, name)
tf.compat.v1.random.multinomial(logits, num_samples, seed, name, output_dtype)
tf.compat.v1.random.poisson(lam, shape, dtype, seed, name)
tf.compat.v1.random.stateless_multinomial(logits, num_samples, seed, output_dtype, name)
tf.compat.v1.scan(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, reverse, name)
tf.compat.v1.setdiff1d(x, y, index_dtype, name)
tf.compat.v1.shape(input, name, out_type)
tf.compat.v1.size(input, name, out_type)
tf.compat.v1.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value, validate_indices, name)
tf.compat.v1.squeeze(input, axis, name, squeeze_dims)
tf.compat.v1.string_split(source, sep, skip_empty, delimiter, result_type, name)
tf.compat.v1.strings.length(input, name, unit)
tf.compat.v1.strings.reduce_join(inputs, axis, keep_dims, separator, name, reduction_indices, keepdims)
tf.compat.v1.strings.split(input, sep, maxsplit, result_type, source, name)
tf.compat.v1.strings.substr(input, pos, len, name, unit)
tf.compat.v1.strings.to_hash_bucket(string_tensor, num_buckets, name, input)
tf.compat.v1.strings.to_number(string_tensor, out_type, name, input)
tf.compat.v1.substr(input, pos, len, name, unit)
tf.compat.v1.to_bfloat16(x, name)
tf.compat.v1.to_complex128(x, name)
tf.compat.v1.to_complex64(x, name)
tf.compat.v1.to_double(x, name)
tf.compat.v1.to_float(x, name)
tf.compat.v1.to_int32(x, name)
tf.compat.v1.to_int64(x, name)
tf.compat.v1.train.sdca_fprint(input, name)
tf.compat.v1.train.sdca_optimizer(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptative, name)
tf.compat.v1.train.sdca_shrink_l1(weights, l1, l2, name)
tf.compat.v1.transpose(a, perm, name, conjugate)
tf.compat.v1.tuple(tensors, name, control_inputs)
tf.compat.v1.where(condition, x, y, name)
tf.compat.v1.zeros_like(tensor, dtype, name, optimize)
tf.concat(values, axis, name)
tf.cond(pred, true_fn, false_fn, name)
tf.conv2d_backprop_filter_v2(input, filter, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
tf.conv2d_backprop_input_v2(input, filter, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
tf.convert_to_tensor(value, dtype, dtype_hint, name)
tf.create_trt_resource_handle(resource_name, name)
tf.debugging.Assert(condition, data, summarize, name)
tf.debugging.assert_all_finite(x, message, name)
tf.debugging.assert_equal(x, y, message, summarize, name)
tf.debugging.assert_greater(x, y, message, summarize, name)
tf.debugging.assert_greater_equal(x, y, message, summarize, name)
tf.debugging.assert_integer(x, message, name)
tf.debugging.assert_less(x, y, message, summarize, name)
tf.debugging.assert_less_equal(x, y, message, summarize, name)
tf.debugging.assert_near(x, y, rtol, atol, message, summarize, name)
tf.debugging.assert_negative(x, message, summarize, name)
tf.debugging.assert_non_negative(x, message, summarize, name)
tf.debugging.assert_non_positive(x, message, summarize, name)
tf.debugging.assert_none_equal(x, y, summarize, message, name)
tf.debugging.assert_positive(x, message, summarize, name)
tf.debugging.assert_proper_iterable(values)
tf.debugging.assert_rank(x, rank, message, name)
tf.debugging.assert_rank_at_least(x, rank, message, name)
tf.debugging.assert_rank_in(x, ranks, message, name)
tf.debugging.assert_same_float_dtype(tensors, dtype)
tf.debugging.assert_scalar(tensor, message, name)
tf.debugging.assert_shapes(shapes, data, summarize, message, name)
tf.debugging.assert_type(tensor, tf_type, message, name)
tf.debugging.check_numerics(tensor, message, name)
tf.dtypes.complex(real, imag, name)
tf.dtypes.saturate_cast(value, dtype, name)
tf.dynamic_partition(data, partitions, num_partitions, name)
tf.dynamic_stitch(indices, data, name)
tf.edit_distance(hypothesis, truth, normalize, name)
tf.ensure_shape(x, shape, name)
tf.expand_dims(input, axis, name)
tf.extract_volume_patches(input, ksizes, strides, padding, name)
tf.eye(num_rows, num_columns, batch_shape, dtype, name)
tf.fill(dims, value, name)
tf.fingerprint(data, method, name)
tf.foldl(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, name)
tf.foldr(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, name)
tf.gather(params, indices, validate_indices, axis, batch_dims, name)
tf.gather_nd(params, indices, batch_dims, name)
tf.get_calibration_data_op(resource_name, name)
tf.histogram_fixed_width(values, value_range, nbins, dtype, name)
tf.histogram_fixed_width_bins(values, value_range, nbins, dtype, name)
tf.identity(input, name)
tf.identity_n(input, name)
tf.image.adjust_brightness(image, delta)
tf.image.adjust_contrast(images, contrast_factor)
tf.image.adjust_gamma(image, gamma, gain)
tf.image.adjust_hue(image, delta, name)
tf.image.adjust_jpeg_quality(image, jpeg_quality, name)
tf.image.adjust_saturation(image, saturation_factor, name)
tf.image.central_crop(image, central_fraction)
tf.image.combined_non_max_suppression(boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, pad_per_class, clip_boxes, name)
tf.image.convert_image_dtype(image, dtype, saturate, name)
tf.image.crop_and_resize(image, boxes, box_indices, crop_size, method, extrapolation_value, name)
tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)
tf.image.draw_bounding_boxes(images, boxes, colors, name)
tf.image.extract_glimpse(input, size, offsets, centered, normalized, noise, name)
tf.image.extract_patches(images, sizes, strides, rates, padding, name)
tf.image.flip_left_right(image)
tf.image.flip_up_down(image)
tf.image.generate_bounding_box_proposals(scores, bbox_deltas, image_info, anchors, nms_threshold, pre_nms_topn, min_size, post_nms_topn, name)
tf.image.grayscale_to_rgb(images, name)
tf.image.hsv_to_rgb(images, name)
tf.image.image_gradients(image)
tf.image.non_max_suppression(boxes, scores, max_output_size, iou_threshold, score_threshold, name)
tf.image.non_max_suppression_overlaps(overlaps, scores, max_output_size, overlap_threshold, score_threshold, name)
tf.image.non_max_suppression_padded(boxes, scores, max_output_size, iou_threshold, score_threshold, pad_to_max_output_size, name, sorted_input, canonicalized_coordinates, tile_size)
tf.image.non_max_suppression_with_scores(boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, name)
tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)
tf.image.per_image_standardization(image)
tf.image.psnr(a, b, max_val, name)
tf.image.random_brightness(image, max_delta, seed)
tf.image.random_contrast(image, lower, upper, seed)
tf.image.random_crop(value, size, seed, name)
tf.image.random_flip_left_right(image, seed)
tf.image.random_flip_up_down(image, seed)
tf.image.random_hue(image, max_delta, seed)
tf.image.random_jpeg_quality(image, min_jpeg_quality, max_jpeg_quality, seed)
tf.image.random_saturation(image, lower, upper, seed)
tf.image.resize(images, size, method, preserve_aspect_ratio, antialias, name)
tf.image.resize_with_crop_or_pad(image, target_height, target_width)
tf.image.resize_with_pad(image, target_height, target_width, method, antialias)
tf.image.rgb_to_grayscale(images, name)
tf.image.rgb_to_hsv(images, name)
tf.image.rgb_to_yiq(images)
tf.image.rgb_to_yuv(images)
tf.image.rot90(image, k, name)
tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed, min_object_covered, aspect_ratio_range, area_range, max_attempts, use_image_if_no_bounding_boxes, name)
tf.image.sobel_edges(image)
tf.image.ssim(img1, img2, max_val, filter_size, filter_sigma, k1, k2, return_index_map)
tf.image.ssim_multiscale(img1, img2, max_val, power_factors, filter_size, filter_sigma, k1, k2)
tf.image.stateless_random_brightness(image, max_delta, seed)
tf.image.stateless_random_contrast(image, lower, upper, seed)
tf.image.stateless_random_crop(value, size, seed, name)
tf.image.stateless_random_flip_left_right(image, seed)
tf.image.stateless_random_flip_up_down(image, seed)
tf.image.stateless_random_hue(image, max_delta, seed)
tf.image.stateless_random_jpeg_quality(image, min_jpeg_quality, max_jpeg_quality, seed)
tf.image.stateless_random_saturation(image, lower, upper, seed)
tf.image.stateless_sample_distorted_bounding_box(image_size, bounding_boxes, seed, min_object_covered, aspect_ratio_range, area_range, max_attempts, use_image_if_no_bounding_boxes, name)
tf.image.total_variation(images, name)
tf.image.transpose(image, name)
tf.image.yiq_to_rgb(images)
tf.image.yuv_to_rgb(images)
tf.initialize_trt_resource(resource_handle, filename, max_cached_engines_count, name)
tf.io.decode_and_crop_jpeg(contents, crop_window, channels, ratio, fancy_upscaling, try_recover_truncated, acceptable_fraction, dct_method, name)
tf.io.decode_base64(input, name)
tf.io.decode_bmp(contents, channels, name)
tf.io.decode_compressed(bytes, compression_type, name)
tf.io.decode_csv(records, record_defaults, field_delim, use_quote_delim, na_value, select_cols, name)
tf.io.decode_gif(contents, name)
tf.io.decode_image(contents, channels, dtype, name, expand_animations)
tf.io.decode_jpeg(contents, channels, ratio, fancy_upscaling, try_recover_truncated, acceptable_fraction, dct_method, name)
tf.io.decode_png(contents, channels, dtype, name)
tf.io.decode_proto(bytes, message_type, field_names, output_types, descriptor_source, message_format, sanitize, name)
tf.io.decode_raw(input_bytes, out_type, little_endian, fixed_length, name)
tf.io.deserialize_many_sparse(serialized_sparse, dtype, rank, name)
tf.io.encode_base64(input, pad, name)
tf.io.encode_jpeg(image, format, quality, progressive, optimize_size, chroma_downsampling, density_unit, x_density, y_density, xmp_metadata, name)
tf.io.encode_png(image, compression, name)
tf.io.encode_proto(sizes, values, field_names, message_type, descriptor_source, name)
tf.io.extract_jpeg_shape(contents, output_type, name)
tf.io.matching_files(pattern, name)
tf.io.parse_example(serialized, features, example_names, name)
tf.io.parse_sequence_example(serialized, context_features, sequence_features, example_names, name)
tf.io.parse_single_example(serialized, features, example_names, name)
tf.io.parse_single_sequence_example(serialized, context_features, sequence_features, example_name, name)
tf.io.parse_tensor(serialized, out_type, name)
tf.io.serialize_many_sparse(sp_input, out_type, name)
tf.io.serialize_sparse(sp_input, out_type, name)
tf.io.write_file(filename, contents, name)
tf.linalg.adjoint(matrix, name)
tf.linalg.band_part(input, num_lower, num_upper, name)
tf.linalg.cholesky(input, name)
tf.linalg.cholesky_solve(chol, rhs, name)
tf.linalg.cross(a, b, name)
tf.linalg.det(input, name)
tf.linalg.diag(diagonal, name, k, num_rows, num_cols, padding_value, align)
tf.linalg.diag_part(input, name, k, padding_value, align)
tf.linalg.eig(tensor, name)
tf.linalg.eigh(tensor, name)
tf.linalg.eigh_tridiagonal(alpha, beta, eigvals_only, select, select_range, tol, name)
tf.linalg.eigvals(tensor, name)
tf.linalg.eigvalsh(tensor, name)
tf.linalg.experimental.conjugate_gradient(operator, rhs, preconditioner, x, tol, max_iter, name)
tf.linalg.expm(input, name)
tf.linalg.global_norm(t_list, name)
tf.linalg.inv(input, adjoint, name)
tf.linalg.logdet(matrix, name)
tf.linalg.logm(input, name)
tf.linalg.lstsq(matrix, rhs, l2_regularizer, fast, name)
tf.linalg.lu(input, output_idx_type, name)
tf.linalg.lu_matrix_inverse(lower_upper, perm, validate_args, name)
tf.linalg.lu_reconstruct(lower_upper, perm, validate_args, name)
tf.linalg.lu_solve(lower_upper, perm, rhs, validate_args, name)
tf.linalg.matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, output_type, name)
tf.linalg.matrix_rank(a, tol, validate_args, name)
tf.linalg.matrix_transpose(a, name, conjugate)
tf.linalg.matvec(a, b, transpose_a, adjoint_a, a_is_sparse, b_is_sparse, name)
tf.linalg.normalize(tensor, ord, axis, name)
tf.linalg.pinv(a, rcond, validate_args, name)
tf.linalg.qr(input, full_matrices, name)
tf.linalg.set_diag(input, diagonal, name, k, align)
tf.linalg.slogdet(input, name)
tf.linalg.solve(matrix, rhs, adjoint, name)
tf.linalg.sqrtm(input, name)
tf.linalg.svd(tensor, full_matrices, compute_uv, name)
tf.linalg.tensor_diag(diagonal, name)
tf.linalg.tensor_diag_part(input, name)
tf.linalg.trace(x, name)
tf.linalg.triangular_solve(matrix, rhs, lower, adjoint, name)
tf.linalg.tridiagonal_matmul(diagonals, rhs, diagonals_format, name)
tf.linalg.tridiagonal_solve(diagonals, rhs, diagonals_format, transpose_rhs, conjugate_rhs, name, partial_pivoting, perturb_singular)
tf.linspace(start, stop, num, name, axis)
tf.math.abs(x, name)
tf.math.accumulate_n(inputs, shape, tensor_dtype, name)
tf.math.acos(x, name)
tf.math.acosh(x, name)
tf.math.add(x, y, name)
tf.math.add_n(inputs, name)
tf.math.angle(input, name)
tf.math.approx_max_k(operand, k, reduction_dimension, recall_target, reduction_input_size_override, aggregate_to_topk, name)
tf.math.approx_min_k(operand, k, reduction_dimension, recall_target, reduction_input_size_override, aggregate_to_topk, name)
tf.math.argmax(input, axis, output_type, name)
tf.math.argmin(input, axis, output_type, name)
tf.math.asin(x, name)
tf.math.asinh(x, name)
tf.math.atan(x, name)
tf.math.atan2(y, x, name)
tf.math.atanh(x, name)
tf.math.bessel_i0(x, name)
tf.math.bessel_i0e(x, name)
tf.math.bessel_i1(x, name)
tf.math.bessel_i1e(x, name)
tf.math.betainc(a, b, x, name)
tf.math.ceil(x, name)
tf.math.confusion_matrix(labels, predictions, num_classes, weights, dtype, name)
tf.math.conj(x, name)
tf.math.cos(x, name)
tf.math.cosh(x, name)
tf.math.count_nonzero(input, axis, keepdims, dtype, name)
tf.math.cumprod(x, axis, exclusive, reverse, name)
tf.math.cumsum(x, axis, exclusive, reverse, name)
tf.math.cumulative_logsumexp(x, axis, exclusive, reverse, name)
tf.math.digamma(x, name)
tf.math.divide(x, y, name)
tf.math.divide_no_nan(x, y, name)
tf.math.equal(x, y, name)
tf.math.erf(x, name)
tf.math.erfc(x, name)
tf.math.erfcinv(x, name)
tf.math.erfinv(x, name)
tf.math.exp(x, name)
tf.math.expm1(x, name)
tf.math.floor(x, name)
tf.math.floordiv(x, y, name)
tf.math.floormod(x, y, name)
tf.math.greater(x, y, name)
tf.math.greater_equal(x, y, name)
tf.math.igamma(a, x, name)
tf.math.igammac(a, x, name)
tf.math.imag(input, name)
tf.math.in_top_k(targets, predictions, k, name)
tf.math.invert_permutation(x, name)
tf.math.is_finite(x, name)
tf.math.is_inf(x, name)
tf.math.is_nan(x, name)
tf.math.is_non_decreasing(x, name)
tf.math.is_strictly_increasing(x, name)
tf.math.l2_normalize(x, axis, epsilon, name, dim)
tf.math.lbeta(x, name)
tf.math.less(x, y, name)
tf.math.less_equal(x, y, name)
tf.math.lgamma(x, name)
tf.math.log(x, name)
tf.math.log1p(x, name)
tf.math.log_sigmoid(x, name)
tf.math.logical_and(x, y, name)
tf.math.logical_not(x, name)
tf.math.logical_or(x, y, name)
tf.math.logical_xor(x, y, name)
tf.math.maximum(x, y, name)
tf.math.minimum(x, y, name)
tf.math.multiply(x, y, name)
tf.math.multiply_no_nan(x, y, name)
tf.math.ndtri(x, name)
tf.math.negative(x, name)
tf.math.nextafter(x1, x2, name)
tf.math.not_equal(x, y, name)
tf.math.polygamma(a, x, name)
tf.math.polyval(coeffs, x, name)
tf.math.pow(x, y, name)
tf.math.real(input, name)
tf.math.reciprocal(x, name)
tf.math.reciprocal_no_nan(x, name)
tf.math.reduce_all(input_tensor, axis, keepdims, name)
tf.math.reduce_any(input_tensor, axis, keepdims, name)
tf.math.reduce_euclidean_norm(input_tensor, axis, keepdims, name)
tf.math.reduce_logsumexp(input_tensor, axis, keepdims, name)
tf.math.reduce_max(input_tensor, axis, keepdims, name)
tf.math.reduce_mean(input_tensor, axis, keepdims, name)
tf.math.reduce_min(input_tensor, axis, keepdims, name)
tf.math.reduce_prod(input_tensor, axis, keepdims, name)
tf.math.reduce_std(input_tensor, axis, keepdims, name)
tf.math.reduce_sum(input_tensor, axis, keepdims, name)
tf.math.reduce_variance(input_tensor, axis, keepdims, name)
tf.math.rint(x, name)
tf.math.round(x, name)
tf.math.rsqrt(x, name)
tf.math.scalar_mul(scalar, x, name)
tf.math.segment_max(data, segment_ids, name)
tf.math.segment_mean(data, segment_ids, name)
tf.math.segment_min(data, segment_ids, name)
tf.math.segment_prod(data, segment_ids, name)
tf.math.segment_sum(data, segment_ids, name)
tf.math.sigmoid(x, name)
tf.math.sign(x, name)
tf.math.sin(x, name)
tf.math.sinh(x, name)
tf.math.sobol_sample(dim, num_results, skip, dtype, name)
tf.math.softplus(features, name)
tf.math.special.bessel_j0(x, name)
tf.math.special.bessel_j1(x, name)
tf.math.special.bessel_k0(x, name)
tf.math.special.bessel_k0e(x, name)
tf.math.special.bessel_k1(x, name)
tf.math.special.bessel_k1e(x, name)
tf.math.special.bessel_y0(x, name)
tf.math.special.bessel_y1(x, name)
tf.math.special.dawsn(x, name)
tf.math.special.expint(x, name)
tf.math.special.fresnel_cos(x, name)
tf.math.special.fresnel_sin(x, name)
tf.math.special.spence(x, name)
tf.math.sqrt(x, name)
tf.math.square(x, name)
tf.math.squared_difference(x, y, name)
tf.math.subtract(x, y, name)
tf.math.tan(x, name)
tf.math.tanh(x, name)
tf.math.top_k(input, k, sorted, name)
tf.math.truediv(x, y, name)
tf.math.unsorted_segment_max(data, segment_ids, num_segments, name)
tf.math.unsorted_segment_mean(data, segment_ids, num_segments, name)
tf.math.unsorted_segment_min(data, segment_ids, num_segments, name)
tf.math.unsorted_segment_prod(data, segment_ids, num_segments, name)
tf.math.unsorted_segment_sqrt_n(data, segment_ids, num_segments, name)
tf.math.unsorted_segment_sum(data, segment_ids, num_segments, name)
tf.math.xdivy(x, y, name)
tf.math.xlog1py(x, y, name)
tf.math.xlogy(x, y, name)
tf.math.zero_fraction(value, name)
tf.math.zeta(x, q, name)
tf.nn.atrous_conv2d(value, filters, rate, padding, name)
tf.nn.atrous_conv2d_transpose(value, filters, output_shape, rate, padding, name)
tf.nn.avg_pool(input, ksize, strides, padding, data_format, name)
tf.nn.avg_pool1d(input, ksize, strides, padding, data_format, name)
tf.nn.avg_pool2d(input, ksize, strides, padding, data_format, name)
tf.nn.avg_pool3d(input, ksize, strides, padding, data_format, name)
tf.nn.batch_norm_with_global_normalization(input, mean, variance, beta, gamma, variance_epsilon, scale_after_normalization, name)
tf.nn.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name)
tf.nn.bias_add(value, bias, data_format, name)
tf.nn.collapse_repeated(labels, seq_length, name)
tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed, name)
tf.nn.compute_average_loss(per_example_loss, sample_weight, global_batch_size)
tf.nn.conv1d(input, filters, stride, padding, data_format, dilations, name)
tf.nn.conv1d_transpose(input, filters, output_shape, strides, padding, data_format, dilations, name)
tf.nn.conv2d(input, filters, strides, padding, data_format, dilations, name)
tf.nn.conv2d_transpose(input, filters, output_shape, strides, padding, data_format, dilations, name)
tf.nn.conv3d(input, filters, strides, padding, data_format, dilations, name)
tf.nn.conv3d_transpose(input, filters, output_shape, strides, padding, data_format, dilations, name)
tf.nn.conv_transpose(input, filters, output_shape, strides, padding, data_format, dilations, name)
tf.nn.convolution(input, filters, strides, padding, data_format, dilations, name)
tf.nn.crelu(features, axis, name)
tf.nn.ctc_beam_search_decoder(inputs, sequence_length, beam_width, top_paths)
tf.nn.ctc_greedy_decoder(inputs, sequence_length, merge_repeated, blank_index)
tf.nn.ctc_loss(labels, logits, label_length, logit_length, logits_time_major, unique, blank_index, name)
tf.nn.ctc_unique_labels(labels, name)
tf.nn.depth_to_space(input, block_size, data_format, name)
tf.nn.depthwise_conv2d(input, filter, strides, padding, data_format, dilations, name)
tf.nn.depthwise_conv2d_backprop_filter(input, filter_sizes, out_backprop, strides, padding, data_format, dilations, name)
tf.nn.depthwise_conv2d_backprop_input(input_sizes, filter, out_backprop, strides, padding, data_format, dilations, name)
tf.nn.dilation2d(input, filters, strides, padding, data_format, dilations, name)
tf.nn.dropout(x, rate, noise_shape, seed, name)
tf.nn.elu(features, name)
tf.nn.embedding_lookup(params, ids, max_norm, name)
tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, combiner, max_norm, name)
tf.nn.erosion2d(value, filters, strides, padding, data_format, dilations, name)
tf.nn.experimental.general_dropout(x, rate, uniform_sampler, noise_shape, name)
tf.nn.experimental.stateless_dropout(x, rate, seed, rng_alg, noise_shape, name)
tf.nn.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, seed, name)
tf.nn.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, seed, name)
tf.nn.gelu(features, approximate, name)
tf.nn.isotonic_regression(inputs, decreasing, axis)
tf.nn.l2_loss(t, name)
tf.nn.leaky_relu(features, alpha, name)
tf.nn.local_response_normalization(input, depth_radius, bias, alpha, beta, name)
tf.nn.log_poisson_loss(targets, log_input, compute_full_loss, name)
tf.nn.log_softmax(logits, axis, name)
tf.nn.max_pool(input, ksize, strides, padding, data_format, name)
tf.nn.max_pool1d(input, ksize, strides, padding, data_format, name)
tf.nn.max_pool2d(input, ksize, strides, padding, data_format, name)
tf.nn.max_pool3d(input, ksize, strides, padding, data_format, name)
tf.nn.max_pool_with_argmax(input, ksize, strides, padding, data_format, output_dtype, include_batch_in_index, name)
tf.nn.moments(x, axes, shift, keepdims, name)
tf.nn.nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, name)
tf.nn.normalize_moments(counts, mean_ss, variance_ss, shift, name)
tf.nn.pool(input, window_shape, pooling_type, strides, padding, data_format, dilations, name)
tf.nn.relu(features, name)
tf.nn.relu6(features, name)
tf.nn.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights, combiner, default_id, max_norm, name)
tf.nn.sampled_softmax_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, seed, name)
tf.nn.scale_regularization_loss(regularization_loss)
tf.nn.selu(features, name)
tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, data_format, dilations, name)
tf.nn.sigmoid_cross_entropy_with_logits(labels, logits, name)
tf.nn.silu(features, beta)
tf.nn.softmax(logits, axis, name)
tf.nn.softmax_cross_entropy_with_logits(labels, logits, axis, name)
tf.nn.softsign(features, name)
tf.nn.space_to_depth(input, block_size, data_format, name)
tf.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name)
tf.nn.sufficient_statistics(x, axes, shift, keepdims, name)
tf.nn.weighted_cross_entropy_with_logits(labels, logits, pos_weight, name)
tf.nn.weighted_moments(x, axes, frequency_weights, keepdims, name)
tf.nn.with_space_to_batch(input, dilation_rate, padding, op, filter_shape, spatial_dims, data_format)
tf.no_op(name)
tf.norm(tensor, ord, axis, keepdims, name)
tf.numpy_function(func, inp, Tout, stateful, name)
tf.one_hot(indices, depth, on_value, off_value, axis, dtype, name)
tf.ones(shape, dtype, name)
tf.ones_like(input, dtype, name)
tf.pad(tensor, paddings, mode, constant_values, name)
tf.parallel_stack(values, name)
tf.py_function(func, inp, Tout, name)
tf.quantization.dequantize(input, min_range, max_range, mode, name, axis, narrow_range, dtype)
tf.quantization.fake_quant_with_min_max_args(inputs, min, max, num_bits, narrow_range, name)
tf.quantization.fake_quant_with_min_max_args_gradient(gradients, inputs, min, max, num_bits, narrow_range, name)
tf.quantization.fake_quant_with_min_max_vars(inputs, min, max, num_bits, narrow_range, name)
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, num_bits, narrow_range, name)
tf.quantization.fake_quant_with_min_max_vars_per_channel(inputs, min, max, num_bits, narrow_range, name)
tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients, inputs, min, max, num_bits, narrow_range, name)
tf.quantization.quantize(input, min_range, max_range, T, mode, round_mode, name, narrow_range, axis, ensure_minimum_range)
tf.quantization.quantize_and_dequantize(input, input_min, input_max, signed_input, num_bits, range_given, round_mode, name, narrow_range, axis)
tf.quantization.quantize_and_dequantize_v2(input, input_min, input_max, signed_input, num_bits, range_given, round_mode, name, narrow_range, axis)
tf.quantization.quantized_concat(concat_dim, values, input_mins, input_maxes, name)
tf.ragged.boolean_mask(data, mask, name)
tf.ragged.constant(pylist, dtype, ragged_rank, inner_shape, name, row_splits_dtype)
tf.ragged.cross(inputs, name)
tf.ragged.cross_hashed(inputs, num_buckets, hash_key, name)
tf.ragged.range(starts, limits, deltas, dtype, name, row_splits_dtype)
tf.ragged.row_splits_to_segment_ids(splits, name, out_type)
tf.ragged.segment_ids_to_row_splits(segment_ids, num_segments, out_type, name)
tf.ragged.stack(values, axis, name)
tf.ragged.stack_dynamic_partitions(data, partitions, num_partitions, name)
tf.random.categorical(logits, num_samples, dtype, seed, name)
tf.random.experimental.index_shuffle(index, seed, max_index)
tf.random.experimental.stateless_shuffle(value, seed, alg, name)
tf.random.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file, distortion, num_reserved_ids, num_shards, shard, unigrams, seed, name)
tf.random.fold_in(seed, data, alg)
tf.random.gamma(shape, alpha, beta, dtype, seed, name)
tf.random.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed, name)
tf.random.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed, name)
tf.random.normal(shape, mean, stddev, dtype, seed, name)
tf.random.poisson(shape, lam, dtype, seed, name)
tf.random.shuffle(value, seed, name)
tf.random.split(seed, num, alg)
tf.random.stateless_binomial(shape, seed, counts, probs, output_dtype, name)
tf.random.stateless_categorical(logits, num_samples, seed, dtype, name)
tf.random.stateless_gamma(shape, seed, alpha, beta, dtype, name)
tf.random.stateless_normal(shape, seed, mean, stddev, dtype, name, alg)
tf.random.stateless_parameterized_truncated_normal(shape, seed, means, stddevs, minvals, maxvals, name)
tf.random.stateless_poisson(shape, seed, lam, dtype, name)
tf.random.stateless_truncated_normal(shape, seed, mean, stddev, dtype, name, alg)
tf.random.stateless_uniform(shape, seed, minval, maxval, dtype, name, alg)
tf.random.truncated_normal(shape, mean, stddev, dtype, seed, name)
tf.random.uniform(shape, minval, maxval, dtype, seed, name)
tf.random.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed, name)
tf.random_index_shuffle(index, seed, max_index, rounds, name)
tf.range(start, limit, delta, dtype, name)
tf.rank(input, name)
tf.realdiv(x, y, name)
tf.repeat(input, repeats, axis, name)
tf.reshape(tensor, shape, name)
tf.reverse(tensor, axis, name)
tf.reverse_sequence(input, seq_lengths, seq_axis, batch_axis, name)
tf.roll(input, shift, axis, name)
tf.scan(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, reverse, name)
tf.scatter_nd(indices, updates, shape, name)
tf.searchsorted(sorted_sequence, values, side, out_type, name)
tf.sequence_mask(lengths, maxlen, dtype, name)
tf.serialize_trt_resource(resource_name, filename, delete_resource, save_gpu_specific_engines, name)
tf.sets.difference(a, b, aminusb, validate_indices)
tf.sets.intersection(a, b, validate_indices)
tf.sets.size(a, validate_indices)
tf.sets.union(a, b, validate_indices)
tf.shape(input, out_type, name)
tf.shape_n(input, out_type, name)
tf.signal.dct(input, type, n, axis, norm, name)
tf.signal.fft(input, name)
tf.signal.fft2d(input, name)
tf.signal.fft3d(input, name)
tf.signal.fftshift(x, axes, name)
tf.signal.frame(signal, frame_length, frame_step, pad_end, pad_value, axis, name)
tf.signal.hamming_window(window_length, periodic, dtype, name)
tf.signal.hann_window(window_length, periodic, dtype, name)
tf.signal.idct(input, type, n, axis, norm, name)
tf.signal.ifft(input, name)
tf.signal.ifft2d(input, name)
tf.signal.ifft3d(input, name)
tf.signal.ifftshift(x, axes, name)
tf.signal.inverse_mdct(mdcts, window_fn, norm, name)
tf.signal.inverse_stft(stfts, frame_length, frame_step, fft_length, window_fn, name)
tf.signal.inverse_stft_window_fn(frame_step, forward_window_fn, name)
tf.signal.irfft(input_tensor, fft_length, name)
tf.signal.irfft2d(input_tensor, fft_length, name)
tf.signal.irfft3d(input_tensor, fft_length, name)
tf.signal.kaiser_bessel_derived_window(window_length, beta, dtype, name)
tf.signal.kaiser_window(window_length, beta, dtype, name)
tf.signal.linear_to_mel_weight_matrix(num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz, upper_edge_hertz, dtype, name)
tf.signal.mdct(signals, frame_length, window_fn, pad_end, norm, name)
tf.signal.mfccs_from_log_mel_spectrograms(log_mel_spectrograms, name)
tf.signal.overlap_and_add(signal, frame_step, name)
tf.signal.rfft(input_tensor, fft_length, name)
tf.signal.rfft2d(input_tensor, fft_length, name)
tf.signal.rfft3d(input_tensor, fft_length, name)
tf.signal.stft(signals, frame_length, frame_step, fft_length, window_fn, pad_end, name)
tf.signal.vorbis_window(window_length, dtype, name)
tf.size(input, out_type, name)
tf.slice(input_, begin, size, name)
tf.sort(values, axis, direction, name)
tf.space_to_batch(input, block_shape, paddings, name)
tf.space_to_batch_nd(input, block_shape, paddings, name)
tf.sparse.reshape(sp_input, shape, name)
tf.split(value, num_or_size_splits, axis, num, name)
tf.squeeze(input, axis, name)
tf.stack(values, axis, name)
tf.stop_gradient(input, name)
tf.stridedslice(input, begin, end, strides, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, var, name)
tf.strings.as_string(input, precision, scientific, shortest, width, fill, name)
tf.strings.bytes_split(input, name)
tf.strings.format(template, inputs, placeholder, summarize, name)
tf.strings.join(inputs, separator, name)
tf.strings.length(input, unit, name)
tf.strings.lower(input, encoding, name)
tf.strings.ngrams(data, ngram_width, separator, pad_values, padding_width, preserve_short_sequences, name)
tf.strings.reduce_join(inputs, axis, keepdims, separator, name)
tf.strings.regex_full_match(input, pattern, name)
tf.strings.regex_replace(input, pattern, rewrite, replace_global, name)
tf.strings.split(input, sep, maxsplit, name)
tf.strings.strip(input, name)
tf.strings.substr(input, pos, len, unit, name)
tf.strings.to_hash_bucket(input, num_buckets, name)
tf.strings.to_hash_bucket_fast(input, num_buckets, name)
tf.strings.to_hash_bucket_strong(input, num_buckets, key, name)
tf.strings.to_number(input, out_type, name)
tf.strings.unicode_decode(input, input_encoding, errors, replacement_char, replace_control_characters, name)
tf.strings.unicode_decode_with_offsets(input, input_encoding, errors, replacement_char, replace_control_characters, name)
tf.strings.unicode_encode(input, output_encoding, errors, replacement_char, name)
tf.strings.unicode_script(input, name)
tf.strings.unicode_split(input, input_encoding, errors, replacement_char, name)
tf.strings.unicode_split_with_offsets(input, input_encoding, errors, replacement_char, name)
tf.strings.unicode_transcode(input, input_encoding, output_encoding, errors, replacement_char, replace_control_characters, name)
tf.strings.unsorted_segment_join(inputs, segment_ids, num_segments, separator, name)
tf.strings.upper(input, encoding, name)
tf.tensor_scatter_nd_add(tensor, indices, updates, name)
tf.tensor_scatter_nd_max(tensor, indices, updates, name)
tf.tensor_scatter_nd_min(tensor, indices, updates, name)
tf.tensor_scatter_nd_sub(tensor, indices, updates, name)
tf.tensor_scatter_nd_update(tensor, indices, updates, name)
tf.tensordot(a, b, axes, name)
tf.tile(input, multiples, name)
tf.timestamp(name)
tf.tpu_partitioned_output_v2(inputs, num_splits, partition_dims, name)
tf.transpose(a, perm, conjugate, name)
tf.trt_engine_op(in_tensor, serialized_segment, OutT, workspace_size_bytes, precision_mode, segment_func, input_shapes, output_shapes, max_cached_engines_count, max_batch_size, calibration_data, use_calibration, segment_funcdef_name, cached_engine_batches, fixed_input_size, static_engine, profile_strategy, use_explicit_precision, name)
tf.truncatediv(x, y, name)
tf.truncatemod(x, y, name)
tf.tuple(tensors, control_inputs, name)
tf.unique(x, out_idx, name)
tf.unique_with_counts(x, out_idx, name)
tf.unravel_index(indices, dims, name)
tf.unstack(value, num, axis, name)
tf.where(condition, x, y, name)
tf.xla_all_reduce(input, group_assignment, reduce_op, mode, name)
tf.xla_broadcast_helper(lhs, rhs, broadcast_dims, name)
tf.xla_call_module(args, version, module, Sout, Tout, dim_args_spec, name)
tf.xla_cluster_output(input, name)
tf.xla_conv(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count, dimension_numbers, precision_config, name)
tf.xla_conv_v2(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count, dimension_numbers, precision_config, preferred_element_type, batch_group_count, name)
tf.xla_custom_call(args, target_name, backend_config, dtype, shape, name)
tf.xla_custom_call_v2(operands, call_target_name, backend_config, has_side_effect, result_dtypes, result_shapes, name)
tf.xla_dequantize(input, min_range, max_range, mode, transpose_output, name)
tf.xla_dot(lhs, rhs, dimension_numbers, precision_config, name)
tf.xla_dot_v2(lhs, rhs, dimension_numbers, precision_config, preferred_element_type, name)
tf.xla_dynamic_slice(input, start_indices, size_indices, name)
tf.xla_dynamic_update_slice(input, update, indices, name)
tf.xla_einsum(a, b, equation, name)
tf.xla_gather(operand, start_indices, slice_sizes, dimension_numbers, indices_are_sorted, name)
tf.xla_if(cond, inputs, then_branch, else_branch, Tout, name)
tf.xla_key_value_sort(keys, values, name)
tf.xla_launch(constants, args, resources, Tresults, function, name)
tf.xla_optimization_barrier(input, name)
tf.xla_pad(input, padding_value, padding_low, padding_high, padding_interior, name)
tf.xla_recv(dtype, tensor_name, shape, name)
tf.xla_reduce(input, init_value, dimensions_to_reduce, reducer, name)
tf.xla_reduce_precision(operand, exponent_bits, mantissa_bits, name)
tf.xla_reduce_scatter(input, group_assignment, scatter_dimension, reduce_op, name)
tf.xla_reduce_window(input, init_value, window_dimensions, window_strides, base_dilations, window_dilations, padding, computation, name)
tf.xla_remove_dynamic_dimension_size(input, dim_index, name)
tf.xla_replica_id(name)
tf.xla_rng_bit_generator(algorithm, initial_state, shape, dtype, name)
tf.xla_scatter(operand, scatter_indices, updates, update_computation, dimension_numbers, indices_are_sorted, name)
tf.xla_select_and_scatter(operand, window_dimensions, window_strides, padding, source, init_value, select, scatter, name)
tf.xla_self_adjoint_eig(a, lower, max_iter, epsilon, name)
tf.xla_send(tensor, tensor_name, name)
tf.xla_set_bound(input, bound, name)
tf.xla_set_dynamic_dimension_size(input, dim_index, size, name)
tf.xla_sharding(input, sharding, unspecified_dims, name)
tf.xla_sort(input, name)
tf.xla_spmd_full_to_shard_shape(input, manual_sharding, dim, unspecified_dims, name)
tf.xla_spmd_shard_to_full_shape(input, manual_sharding, full_shape, dim, unspecified_dims, name)
tf.xla_svd(a, max_iter, epsilon, precision_config, name)
tf.xla_variadic_reduce(input, init_value, dimensions_to_reduce, reducer, name)
tf.xla_variadic_reduce_v2(inputs, init_values, dimensions_to_reduce, reducer, name)
tf.xla_variadic_sort(inputs, dimension, comparator, is_stable, name)
tf.xla_while(input, cond, body, name)
tf.zeros(shape, dtype, name)
tf.zeros_like(input, dtype, name)