neulab/docprompting-conala huggingface dataset:
tf.raw_ops.BoostedTreesQuantileStreamResourceDeserialize Deserialize bucket boundaries and ready flag into current QuantileAccumulator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceDeserialize  
tf.raw_ops.BoostedTreesQuantileStreamResourceDeserialize(
    quantile_stream_resource_handle, bucket_boundaries, name=None
)
 An op that deserializes bucket boundaries and are boundaries ready flag into current QuantileAccumulator.
 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource handle referring to a QuantileStreamResource.  
  bucket_boundaries   A list of at least 1 Tensor objects with type float32. float; List of Rank 1 Tensors each containing the bucket boundaries for a feature.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesQuantileStreamResourceFlush Flush the summaries for a quantile stream resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceFlush  
tf.raw_ops.BoostedTreesQuantileStreamResourceFlush(
    quantile_stream_resource_handle, num_buckets, generate_quantiles=False,
    name=None
)
 An op that flushes the summaries for a quantile stream resource.
 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource handle referring to a QuantileStreamResource.  
  num_buckets   A Tensor of type int64. int; approximate number of buckets unless using generate_quantiles.  
  generate_quantiles   An optional bool. Defaults to False. bool; If True, the output will be the num_quantiles for each stream where the ith entry is the ith quantile of the input with an approximation error of epsilon. Duplicate values may be present. If False, the output will be the points in the histogram that we got which roughly translates to 1/epsilon boundaries and without any duplicates. Default to False.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesQuantileStreamResourceGetBucketBoundaries Generate the bucket boundaries for each feature based on accumulated summaries.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceGetBucketBoundaries  
tf.raw_ops.BoostedTreesQuantileStreamResourceGetBucketBoundaries(
    quantile_stream_resource_handle, num_features, name=None
)
 An op that returns a list of float tensors for a quantile stream resource. Each tensor is Rank 1 containing bucket boundaries for a single feature.
 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource handle referring to a QuantileStreamResource.  
  num_features   An int that is >= 0. inferred int; number of features to get bucket boundaries for.  
  name   A name for the operation (optional).   
 


 Returns   A list of num_features Tensor objects with type float32.
tf.raw_ops.BoostedTreesQuantileStreamResourceHandleOp Creates a handle to a BoostedTreesQuantileStreamResource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceHandleOp  
tf.raw_ops.BoostedTreesQuantileStreamResourceHandleOp(
    container='', shared_name='', name=None
)

 


 Args
  container   An optional string. Defaults to "".  
  shared_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.BoostedTreesSerializeEnsemble Serializes the tree ensemble to a proto.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesSerializeEnsemble  
tf.raw_ops.BoostedTreesSerializeEnsemble(
    tree_ensemble_handle, name=None
)

 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the tree ensemble.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (stamp_token, tree_ensemble_serialized).     stamp_token   A Tensor of type int64.  
  tree_ensemble_serialized   A Tensor of type string.
tf.raw_ops.BoostedTreesSparseAggregateStats Aggregates the summary of accumulated stats for the batch.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesSparseAggregateStats  
tf.raw_ops.BoostedTreesSparseAggregateStats(
    node_ids, gradients, hessians, feature_indices, feature_values, feature_shape,
    max_splits, num_buckets, name=None
)
 The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id.
 


 Args
  node_ids   A Tensor of type int32. int32; Rank 1 Tensor containing node ids for each example, shape [batch_size].  
  gradients   A Tensor of type float32. float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example.  
  hessians   A Tensor of type float32. float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example.  
  feature_indices   A Tensor of type int32. int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]). Number of sparse entries across all instances from the batch. The first value is the index of the instance, the second is dimension of the feature. The second axis can only have 2 values, i.e., the input dense version of Tensor can only be matrix.  
  feature_values   A Tensor of type int32. int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]). Number of sparse entries across all instances from the batch. The first value is the index of the instance, the second is dimension of the feature.  
  feature_shape   A Tensor of type int32. int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]). The first axis can only have 2 values, [batch_size, feature_dimension].  
  max_splits   An int that is >= 1. int; the maximum number of splits possible in the whole tree.  
  num_buckets   An int that is >= 1. int; equals to the maximum possible value of bucketized feature + 1.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (stats_summary_indices, stats_summary_values, stats_summary_shape).     stats_summary_indices   A Tensor of type int32.  
  stats_summary_values   A Tensor of type float32.  
  stats_summary_shape   A Tensor of type int32.
tf.raw_ops.BoostedTreesTrainingPredict Runs multiple additive regression ensemble predictors on input instances and  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesTrainingPredict  
tf.raw_ops.BoostedTreesTrainingPredict(
    tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features,
    logits_dimension, name=None
)
 computes the update to cached logits. It is designed to be used during training. It traverses the trees starting from cached tree id and cached node id and calculates the updates to be pushed to the cache.
 


 Args
  tree_ensemble_handle   A Tensor of type resource.  
  cached_tree_ids   A Tensor of type int32. Rank 1 Tensor containing cached tree ids which is the starting tree of prediction.  
  cached_node_ids   A Tensor of type int32. Rank 1 Tensor containing cached node id which is the starting node of prediction.  
  bucketized_features   A list of at least 1 Tensor objects with type int32. A list of rank 1 Tensors containing bucket id for each feature.  
  logits_dimension   An int. scalar, dimension of the logits, to be used for partial logits shape.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (partial_logits, tree_ids, node_ids).     partial_logits   A Tensor of type float32.  
  tree_ids   A Tensor of type int32.  
  node_ids   A Tensor of type int32.
tf.raw_ops.BoostedTreesUpdateEnsemble Updates the tree ensemble by either adding a layer to the last tree being grown  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesUpdateEnsemble  
tf.raw_ops.BoostedTreesUpdateEnsemble(
    tree_ensemble_handle, feature_ids, node_ids, gains, thresholds,
    left_node_contribs, right_node_contribs, max_depth, learning_rate, pruning_mode,
    name=None
)
 or by starting a new tree.
 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the ensemble variable.  
  feature_ids   A Tensor of type int32. Rank 1 tensor with ids for each feature. This is the real id of the feature that will be used in the split.  
  node_ids   A list of Tensor objects with type int32. List of rank 1 tensors representing the nodes for which this feature has a split.  
  gains   A list with the same length as node_ids of Tensor objects with type float32. List of rank 1 tensors representing the gains for each of the feature's split.  
  thresholds   A list with the same length as node_ids of Tensor objects with type int32. List of rank 1 tensors representing the thesholds for each of the feature's split.  
  left_node_contribs   A list with the same length as node_ids of Tensor objects with type float32. List of rank 2 tensors with left leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the left nodes.  
  right_node_contribs   A list with the same length as node_ids of Tensor objects with type float32. List of rank 2 tensors with right leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the right nodes.  
  max_depth   A Tensor of type int32. Max depth of the tree to build.  
  learning_rate   A Tensor of type float32. shrinkage const for each new tree.  
  pruning_mode   An int that is >= 0. 0-No pruning, 1-Pre-pruning, 2-Post-pruning.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesUpdateEnsembleV2 Updates the tree ensemble by adding a layer to the last tree being grown  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesUpdateEnsembleV2  
tf.raw_ops.BoostedTreesUpdateEnsembleV2(
    tree_ensemble_handle, feature_ids, dimension_ids, node_ids, gains, thresholds,
    left_node_contribs, right_node_contribs, split_types, max_depth, learning_rate,
    pruning_mode, logits_dimension=1, name=None
)
 or by starting a new tree.
 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the ensemble variable.  
  feature_ids   A list of at least 1 Tensor objects with type int32. Rank 1 tensor with ids for each feature. This is the real id of the feature that will be used in the split.  
  dimension_ids   A list of Tensor objects with type int32. List of rank 1 tensors representing the dimension in each feature.  
  node_ids   A list with the same length as dimension_ids of Tensor objects with type int32. List of rank 1 tensors representing the nodes for which this feature has a split.  
  gains   A list with the same length as dimension_ids of Tensor objects with type float32. List of rank 1 tensors representing the gains for each of the feature's split.  
  thresholds   A list with the same length as dimension_ids of Tensor objects with type int32. List of rank 1 tensors representing the thesholds for each of the feature's split.  
  left_node_contribs   A list with the same length as dimension_ids of Tensor objects with type float32. List of rank 2 tensors with left leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the left nodes.  
  right_node_contribs   A list with the same length as dimension_ids of Tensor objects with type float32. List of rank 2 tensors with right leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the right nodes.  
  split_types   A list with the same length as dimension_ids of Tensor objects with type string. List of rank 1 tensors representing the split type for each feature.  
  max_depth   A Tensor of type int32. Max depth of the tree to build.  
  learning_rate   A Tensor of type float32. shrinkage const for each new tree.  
  pruning_mode   A Tensor of type int32. 0-No pruning, 1-Pre-pruning, 2-Post-pruning.  
  logits_dimension   An optional int. Defaults to 1. scalar, dimension of the logits  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BroadcastArgs Return the shape of s0 op s1 with broadcast.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BroadcastArgs  
tf.raw_ops.BroadcastArgs(
    s0, s1, name=None
)
 Given s0 and s1, tensors that represent shapes, compute r0, the broadcasted shape. s0, s1 and r0 are all integer vectors.
 


 Args
  s0   A Tensor. Must be one of the following types: int32, int64.  
  s1   A Tensor. Must have the same type as s0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as s0.
tf.raw_ops.BroadcastGradientArgs Return the reduction indices for computing gradients of s0 op s1 with broadcast.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BroadcastGradientArgs  
tf.raw_ops.BroadcastGradientArgs(
    s0, s1, name=None
)
 This is typically used by gradient computations for a broadcasting operation.
 


 Args
  s0   A Tensor. Must be one of the following types: int32, int64.  
  s1   A Tensor. Must have the same type as s0.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (r0, r1).     r0   A Tensor. Has the same type as s0.  
  r1   A Tensor. Has the same type as s0.
tf.raw_ops.BroadcastTo Broadcast an array for a compatible shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BroadcastTo  
tf.raw_ops.BroadcastTo(
    input, shape, name=None
)
 Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward. For example, 
x = tf.constant([1, 2, 3])
y = tf.broadcast_to(x, [3, 3])
print(y)
tf.Tensor(
    [[1 2 3]
     [1 2 3]
     [1 2 3]], shape=(3, 3), dtype=int32)
 In the above example, the input Tensor with the shape of [1, 3] is broadcasted to output Tensor with shape of [3, 3]. When doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized. However, broadcast_to does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, broadcast_to might be fused to subsequent operation and then be optimized away, however.)
 


 Args
  input   A Tensor. A Tensor to broadcast.  
  shape   A Tensor. Must be one of the following types: int32, int64. An 1-D int Tensor. The shape of the desired output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Bucketize Bucketizes 'input' based on 'boundaries'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Bucketize  
tf.raw_ops.Bucketize(
    input, boundaries, name=None
)
 For example, if the inputs are boundaries = [0, 10, 100] input = [[-5, 10000] [150, 10] [5, 100]] then the output will be output = [[0, 3] [3, 2] [1, 3]]
 


 Args
  input   A Tensor. Must be one of the following types: int32, int64, float32, float64. Any shape of Tensor contains with int or float type.  
  boundaries   A list of floats. A sorted list of floats gives the boundary of the buckets.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.BytesProducedStatsDataset Records the bytes size of each element of input_dataset in a StatsAggregator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BytesProducedStatsDataset  
tf.raw_ops.BytesProducedStatsDataset(
    input_dataset, tag, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  tag   A Tensor of type string.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.CacheDataset Creates a dataset that caches elements from input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CacheDataset  
tf.raw_ops.CacheDataset(
    input_dataset, filename, output_types, output_shapes, name=None
)
 A CacheDataset will iterate over the input_dataset, and store tensors. If the cache already exists, the cache will be used. If the cache is inappropriate (e.g. cannot be opened, contains tensors of the wrong shape / size), an error will the returned when used.
 


 Args
  input_dataset   A Tensor of type variant.  
  filename   A Tensor of type string. A path on the filesystem where we should cache the dataset. Note: this will be a directory.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.CacheDatasetV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CacheDatasetV2  
tf.raw_ops.CacheDatasetV2(
    input_dataset, filename, cache, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  filename   A Tensor of type string.  
  cache   A Tensor of type resource.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.Case An n-way switch statement which calls a single branch function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Case  
tf.raw_ops.Case(
    branch_index, input, Tout, branches, output_shapes=[], name=None
)
 An n-way switch statement, implementing the following:

```
switch (branch_index) {
  case 0:
    output = branches[0](input);
    break;
  case 1:
    output = branches[1](input);
    break;
  ...
  case [[nbranches-1]]:
  default:
    output = branches[nbranches-1](input);
    break;
}
```

 


 Args
  branch_index   A Tensor of type int32. The branch selector, an int32 Tensor.  
  input   A list of Tensor objects. A list of input tensors passed to the branch function.  
  Tout   A list of tf.DTypes. A list of output types.  
  branches   A list of functions decorated with @Defun that has length >= 1. A list of functions each of which takes 'inputs' and returns a list of tensors, whose types are the same as what every other branch returns.  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.Cast Cast x of type SrcT to y of DstT.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cast  
tf.raw_ops.Cast(
    x, DstT, Truncate=False, name=None
)

 


 Args
  x   A Tensor.  
  DstT   A tf.DType.  
  Truncate   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type DstT.
tf.raw_ops.Ceil Returns element-wise smallest integer not less than x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Ceil  
tf.raw_ops.Ceil(
    x, name=None
)

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.CheckNumerics Checks a tensor for NaN and Inf values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CheckNumerics  
tf.raw_ops.CheckNumerics(
    tensor, message, name=None
)
 When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.
 


 Args
  tensor   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  message   A string. Prefix of the error message.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.CheckNumericsV2 Checks a tensor for NaN, -Inf and +Inf values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CheckNumericsV2  
tf.raw_ops.CheckNumericsV2(
    tensor, message, name=None
)
 When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is. Unlike CheckNumerics (V1), CheckNumericsV2 distinguishes -Inf and +Inf in the errors it throws.
 


 Args
  tensor   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  message   A string. Prefix of the error message.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.Cholesky Computes the Cholesky decomposition of one or more square matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cholesky  
tf.raw_ops.Cholesky(
    input, name=None
)
 The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The input has to be symmetric and positive definite. Only the lower-triangular part of the input will be used for this operation. The upper-triangular part will not be read. The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices [..., :, :]. 
Note: The gradient computation on GPU is faster for large matrices but not for large batch dimensions when the submatrices are small. In this case it might be faster to use the CPU.

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Shape is [..., M, M].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CholeskyGrad Computes the reverse mode backpropagated gradient of the Cholesky algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CholeskyGrad  
tf.raw_ops.CholeskyGrad(
    l, grad, name=None
)
 For an explanation see "Differentiation of the Cholesky algorithm" by Iain Murray http://arxiv.org/abs/1602.07527
 


 Args
  l   A Tensor. Must be one of the following types: half, float32, float64. Output of batch Cholesky algorithm l = cholesky(A). Shape is [..., M, M]. Algorithm depends only on lower triangular part of the innermost matrices of this tensor.  
  grad   A Tensor. Must have the same type as l. df/dl where f is some scalar function. Shape is [..., M, M]. Algorithm depends only on lower triangular part of the innermost matrices of this tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as l.