neulab/docprompting-conala huggingface dataset:
tf.raw_ops.BlockLSTMGrad Computes the LSTM cell backward propagation for the entire time sequence.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BlockLSTMGrad  
tf.raw_ops.BlockLSTMGrad(
    seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h,
    cs_grad, h_grad, use_peephole, name=None
)
 This implementation is to be used in conjunction of LSTMBlock.
 


 Args
  seq_len_max   A Tensor of type int64. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.  
  x   A Tensor. Must be one of the following types: half, float32. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. Value of the initial cell state.  
  h_prev   A Tensor. Must have the same type as x. Initial output of cell (to be used for peephole).  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  i   A Tensor. Must have the same type as x. The input gate over the whole time sequence.  
  cs   A Tensor. Must have the same type as x. The cell state before the tanh over the whole time sequence.  
  f   A Tensor. Must have the same type as x. The forget gate over the whole time sequence.  
  o   A Tensor. Must have the same type as x. The output gate over the whole time sequence.  
  ci   A Tensor. Must have the same type as x. The cell input over the whole time sequence.  
  co   A Tensor. Must have the same type as x. The cell after the tanh over the whole time sequence.  
  h   A Tensor. Must have the same type as x. The output h vector over the whole time sequence.  
  cs_grad   A Tensor. Must have the same type as x. The current gradient of cs.  
  h_grad   A Tensor. Must have the same type as x. The gradient of h vector.  
  use_peephole   A bool. Whether to use peephole weights.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).     x_grad   A Tensor. Has the same type as x.  
  cs_prev_grad   A Tensor. Has the same type as x.  
  h_prev_grad   A Tensor. Has the same type as x.  
  w_grad   A Tensor. Has the same type as x.  
  wci_grad   A Tensor. Has the same type as x.  
  wcf_grad   A Tensor. Has the same type as x.  
  wco_grad   A Tensor. Has the same type as x.  
  b_grad   A Tensor. Has the same type as x.
tf.raw_ops.BlockLSTMGradV2 Computes the LSTM cell backward propagation for the entire time sequence.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BlockLSTMGradV2  
tf.raw_ops.BlockLSTMGradV2(
    seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h,
    cs_grad, h_grad, use_peephole, name=None
)
 This implementation is to be used in conjunction of BlockLSTMV2.
 


 Args
  seq_len_max   A Tensor of type int64. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.  
  x   A Tensor. Must be one of the following types: half, float32. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. Value of the initial cell state.  
  h_prev   A Tensor. Must have the same type as x. Initial output of cell (to be used for peephole).  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  i   A Tensor. Must have the same type as x. The input gate over the whole time sequence.  
  cs   A Tensor. Must have the same type as x. The cell state before the tanh over the whole time sequence.  
  f   A Tensor. Must have the same type as x. The forget gate over the whole time sequence.  
  o   A Tensor. Must have the same type as x. The output gate over the whole time sequence.  
  ci   A Tensor. Must have the same type as x. The cell input over the whole time sequence.  
  co   A Tensor. Must have the same type as x. The cell after the tanh over the whole time sequence.  
  h   A Tensor. Must have the same type as x. The output h vector over the whole time sequence.  
  cs_grad   A Tensor. Must have the same type as x. The current gradient of cs.  
  h_grad   A Tensor. Must have the same type as x. The gradient of h vector.  
  use_peephole   A bool. Whether to use peephole weights.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).     x_grad   A Tensor. Has the same type as x.  
  cs_prev_grad   A Tensor. Has the same type as x.  
  h_prev_grad   A Tensor. Has the same type as x.  
  w_grad   A Tensor. Has the same type as x.  
  wci_grad   A Tensor. Has the same type as x.  
  wcf_grad   A Tensor. Has the same type as x.  
  wco_grad   A Tensor. Has the same type as x.  
  b_grad   A Tensor. Has the same type as x.
tf.raw_ops.BlockLSTMV2 Computes the LSTM cell forward propagation for all the time steps.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BlockLSTMV2  
tf.raw_ops.BlockLSTMV2(
    seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, cell_clip=0,
    use_peephole=False, name=None
)
 This is equivalent to applying LSTMBlockCell in a loop, like so: for x1 in unpack(x):
  i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
    x1, cs_prev, h_prev, w, wci, wcf, wco, b)
  cs_prev = cs1
  h_prev = h1
  i.append(i1)
  cs.append(cs1)
  f.append(f1)
  o.append(o1)
  ci.append(ci1)
  co.append(co1)
  h.append(h1)
return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)

Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout,
this op uses IFCO. So in order for the following snippet to be equivalent
all gate-related outputs should be reordered.

 


 Args
  seq_len_max   A Tensor of type int64. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.  
  x   A Tensor. Must be one of the following types: half, float32. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. Value of the initial cell state.  
  h_prev   A Tensor. Must have the same type as x. Initial output of cell (to be used for peephole).  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  cell_clip   An optional float. Defaults to 0. Value to clip the 'cs' value to.  
  use_peephole   An optional bool. Defaults to False. Whether to use peephole weights.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (i, cs, f, o, ci, co, h).     i   A Tensor. Has the same type as x.  
  cs   A Tensor. Has the same type as x.  
  f   A Tensor. Has the same type as x.  
  o   A Tensor. Has the same type as x.  
  ci   A Tensor. Has the same type as x.  
  co   A Tensor. Has the same type as x.  
  h   A Tensor. Has the same type as x.
tf.raw_ops.BoostedTreesAggregateStats 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.BoostedTreesAggregateStats  
tf.raw_ops.BoostedTreesAggregateStats(
    node_ids, gradients, hessians, feature, max_splits, num_buckets, name=None
)
 The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket.
 


 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   A Tensor of type int32. int32; Rank 2 feature Tensors (shape=[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.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.BoostedTreesBucketize Bucketize each feature based on bucket boundaries.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesBucketize  
tf.raw_ops.BoostedTreesBucketize(
    float_values, bucket_boundaries, name=None
)
 An op that returns a list of float tensors, where each tensor represents the bucketized values for a single feature.
 


 Args
  float_values   A list of Tensor objects with type float32. float; List of Rank 1 Tensor each containing float values for a single feature.  
  bucket_boundaries   A list with the same length as float_values of Tensor objects with type float32. float; List of Rank 1 Tensors each containing the bucket boundaries for a single feature.  
  name   A name for the operation (optional).   
 


 Returns   A list with the same length as float_values of Tensor objects with type int32.
tf.raw_ops.BoostedTreesCalculateBestFeatureSplit Calculates gains for each feature and returns the best possible split information for the feature.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesCalculateBestFeatureSplit  
tf.raw_ops.BoostedTreesCalculateBestFeatureSplit(
    node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight,
    logits_dimension, split_type='inequality', name=None
)
 The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split. In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature.
 


 Args
  node_id_range   A Tensor of type int32. A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within stats_summary_list. The nodes are iterated between the two nodes specified by the tensor, as like for node_id in range(node_id_range[0], node_id_range[1]) (Note that the last index node_id_range[1] is exclusive).  
  stats_summary   A Tensor of type float32. A Rank 4 tensor (#shape=[max_splits, feature_dims, bucket, stats_dims]) for accumulated stats summary (gradient/hessian) per node, per dimension, per buckets for each feature. The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used.  
  l1   A Tensor of type float32. l1 regularization factor on leaf weights, per instance based.  
  l2   A Tensor of type float32. l2 regularization factor on leaf weights, per instance based.  
  tree_complexity   A Tensor of type float32. adjustment to the gain, per leaf based.  
  min_node_weight   A Tensor of type float32. minimum avg of hessians in a node before required for the node to be considered for splitting.  
  logits_dimension   An int that is >= 1. The dimension of logit, i.e., number of classes.  
  split_type   An optional string from: "inequality", "equality". Defaults to "inequality". A string indicating if this Op should perform inequality split or equality split.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (node_ids, gains, feature_dimensions, thresholds, left_node_contribs, right_node_contribs, split_with_default_directions).     node_ids   A Tensor of type int32.  
  gains   A Tensor of type float32.  
  feature_dimensions   A Tensor of type int32.  
  thresholds   A Tensor of type int32.  
  left_node_contribs   A Tensor of type float32.  
  right_node_contribs   A Tensor of type float32.  
  split_with_default_directions   A Tensor of type string.
tf.raw_ops.BoostedTreesCenterBias Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesCenterBias  
tf.raw_ops.BoostedTreesCenterBias(
    tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, name=None
)

 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the tree ensemble.  
  mean_gradients   A Tensor of type float32. A tensor with shape=[logits_dimension] with mean of gradients for a first node.  
  mean_hessians   A Tensor of type float32. A tensor with shape=[logits_dimension] mean of hessians for a first node.  
  l1   A Tensor of type float32. l1 regularization factor on leaf weights, per instance based.  
  l2   A Tensor of type float32. l2 regularization factor on leaf weights, per instance based.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.BoostedTreesCreateEnsemble Creates a tree ensemble model and returns a handle to it.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesCreateEnsemble  
tf.raw_ops.BoostedTreesCreateEnsemble(
    tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None
)

 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the tree ensemble resource to be created.  
  stamp_token   A Tensor of type int64. Token to use as the initial value of the resource stamp.  
  tree_ensemble_serialized   A Tensor of type string. Serialized proto of the tree ensemble.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesCreateQuantileStreamResource Create the Resource for Quantile Streams.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesCreateQuantileStreamResource  
tf.raw_ops.BoostedTreesCreateQuantileStreamResource(
    quantile_stream_resource_handle, epsilon, num_streams,
    max_elements=1099511627776, name=None
)

 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource; Handle to quantile stream resource.  
  epsilon   A Tensor of type float32. float; The required approximation error of the stream resource.  
  num_streams   A Tensor of type int64. int; The number of streams managed by the resource that shares the same epsilon.  
  max_elements   An optional int. Defaults to 1099511627776. int; The maximum number of data points that can be fed to the stream.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesDeserializeEnsemble Deserializes a serialized tree ensemble config and replaces current tree  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesDeserializeEnsemble  
tf.raw_ops.BoostedTreesDeserializeEnsemble(
    tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None
)
 ensemble.
 


 Args
  tree_ensemble_handle   A Tensor of type resource. Handle to the tree ensemble.  
  stamp_token   A Tensor of type int64. Token to use as the new value of the resource stamp.  
  tree_ensemble_serialized   A Tensor of type string. Serialized proto of the ensemble.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.BoostedTreesEnsembleResourceHandleOp Creates a handle to a BoostedTreesEnsembleResource  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesEnsembleResourceHandleOp  
tf.raw_ops.BoostedTreesEnsembleResourceHandleOp(
    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.BoostedTreesExampleDebugOutputs Debugging/model interpretability outputs for each example.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesExampleDebugOutputs  
tf.raw_ops.BoostedTreesExampleDebugOutputs(
    tree_ensemble_handle, bucketized_features, logits_dimension, name=None
)
 It traverses all the trees and computes debug metrics for individual examples, such as getting split feature ids and logits after each split along the decision path used to compute directional feature contributions.
 


 Args
  tree_ensemble_handle   A Tensor of type resource.  
  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 constructing the protos in examples_debug_outputs_serialized.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.BoostedTreesFlushQuantileSummaries Flush the quantile summaries from each quantile stream resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesFlushQuantileSummaries  
tf.raw_ops.BoostedTreesFlushQuantileSummaries(
    quantile_stream_resource_handle, num_features, name=None
)
 An op that outputs a list of quantile summaries of a quantile stream resource. Each summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank) 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.  
  name   A name for the operation (optional).   
 


 Returns   A list of num_features Tensor objects with type float32.
tf.raw_ops.BoostedTreesGetEnsembleStates Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesGetEnsembleStates  
tf.raw_ops.BoostedTreesGetEnsembleStates(
    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, num_trees, num_finalized_trees, num_attempted_layers, last_layer_nodes_range).     stamp_token   A Tensor of type int64.  
  num_trees   A Tensor of type int32.  
  num_finalized_trees   A Tensor of type int32.  
  num_attempted_layers   A Tensor of type int32.  
  last_layer_nodes_range   A Tensor of type int32.
tf.raw_ops.BoostedTreesMakeQuantileSummaries Makes the summary of quantiles for the batch.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesMakeQuantileSummaries  
tf.raw_ops.BoostedTreesMakeQuantileSummaries(
    float_values, example_weights, epsilon, name=None
)
 An op that takes a list of tensors (one tensor per feature) and outputs the quantile summaries for each tensor.
 


 Args
  float_values   A list of Tensor objects with type float32. float; List of Rank 1 Tensors each containing values for a single feature.  
  example_weights   A Tensor of type float32. float; Rank 1 Tensor with weights per instance.  
  epsilon   A Tensor of type float32. float; The required maximum approximation error.  
  name   A name for the operation (optional).   
 


 Returns   A list with the same length as float_values of Tensor objects with type float32.
tf.raw_ops.BoostedTreesMakeStatsSummary Makes 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.BoostedTreesMakeStatsSummary  
tf.raw_ops.BoostedTreesMakeStatsSummary(
    node_ids, gradients, hessians, bucketized_features_list, max_splits,
    num_buckets, name=None
)
 The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.
 


 Args
  node_ids   A Tensor of type int32. int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer.  
  gradients   A Tensor of type float32. float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients.  
  hessians   A Tensor of type float32. float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians.  
  bucketized_features_list   A list of at least 1 Tensor objects with type int32. int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column).  
  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.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.BoostedTreesPredict 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.BoostedTreesPredict  
tf.raw_ops.BoostedTreesPredict(
    tree_ensemble_handle, bucketized_features, logits_dimension, name=None
)
 computes the logits. It is designed to be used during prediction. It traverses all the trees and calculates the final score for each instance.
 


 Args
  tree_ensemble_handle   A Tensor of type resource.  
  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 Tensor of type float32.
tf.raw_ops.BoostedTreesQuantileStreamResourceAddSummaries Add the quantile summaries to each quantile stream resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceAddSummaries  
tf.raw_ops.BoostedTreesQuantileStreamResourceAddSummaries(
    quantile_stream_resource_handle, summaries, name=None
)
 An op that adds a list of quantile summaries to a quantile stream resource. Each summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank) for a single feature.
 


 Args
  quantile_stream_resource_handle   A Tensor of type resource. resource handle referring to a QuantileStreamResource.  
  summaries   A list of Tensor objects with type float32. string; List of Rank 2 Tensor each containing the summaries for a single feature.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.