neulab/docprompting-conala huggingface dataset:
tf.raw_ops.StatelessTruncatedNormalV2 Outputs deterministic pseudorandom values from a truncated normal distribution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessTruncatedNormalV2  
tf.raw_ops.StatelessTruncatedNormalV2(
    shape, key, counter, alg, dtype=tf.dtypes.float32, name=None
)
 The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. The outputs are a deterministic function of shape, key, counter and alg.
 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.  
  key   A Tensor of type uint64. Key for the counter-based RNG algorithm (shape uint64[1]).  
  counter   A Tensor of type uint64. Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.  
  alg   A Tensor of type int32. The RNG algorithm (shape int32[]).  
  dtype   An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32. The type of the output.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.StatelessWhile output = input; While (Cond(output)) { output = Body(output) }  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatelessWhile  
tf.raw_ops.StatelessWhile(
    input, cond, body, output_shapes=[], parallel_iterations=10, name=None
)

 


 Args
  input   A list of Tensor objects. A list of input tensors whose types are T.  
  cond   A function decorated with @Defun. A function takes 'input' and returns a tensor. If the tensor is a scalar of non-boolean, the scalar is converted to a boolean according to the following rule: if the scalar is a numerical value, non-zero means True and zero means False; if the scalar is a string, non-empty means True and empty means False. If the tensor is not a scalar, non-emptiness means True and False otherwise. This should only be used when the while condition and body functions do not have stateful ops. 
 
  body   A function decorated with @Defun. A function that takes a list of tensors and returns another list of tensors. Both lists have the same types as specified by T.  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].  
  parallel_iterations   An optional int. Defaults to 10.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects. Has the same type as input.
tf.raw_ops.StaticRegexFullMatch Check if the input matches the regex pattern.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StaticRegexFullMatch  
tf.raw_ops.StaticRegexFullMatch(
    input, pattern, name=None
)
 The input is a string tensor of any shape. The pattern is the regular expression to be matched with every element of the input tensor. The boolean values (True or False) of the output tensor indicate if the input matches the regex pattern provided. The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
 


 Args
  input   A Tensor of type string. A string tensor of the text to be processed.  
  pattern   A string. The regular expression to match the input.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.StaticRegexReplace Replaces the match of pattern in input with rewrite.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StaticRegexReplace  
tf.raw_ops.StaticRegexReplace(
    input, pattern, rewrite, replace_global=True, name=None
)
 It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
 


 Args
  input   A Tensor of type string. The text to be processed.  
  pattern   A string. The regular expression to match the input.  
  rewrite   A string. The rewrite to be applied to the matched expression.  
  replace_global   An optional bool. Defaults to True. If True, the replacement is global, otherwise the replacement is done only on the first match.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StatsAggregatorHandle Creates a statistics manager resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorHandle  
tf.raw_ops.StatsAggregatorHandle(
    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.StatsAggregatorHandleV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorHandleV2  
tf.raw_ops.StatsAggregatorHandleV2(
    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.StatsAggregatorSetSummaryWriter Set a summary_writer_interface to record statistics using given stats_aggregator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorSetSummaryWriter  
tf.raw_ops.StatsAggregatorSetSummaryWriter(
    stats_aggregator, summary, name=None
)

 


 Args
  stats_aggregator   A Tensor of type resource.  
  summary   A Tensor of type resource.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.StatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StatsAggregatorSummary  
tf.raw_ops.StatsAggregatorSummary(
    iterator, name=None
)

 


 Args
  iterator   A Tensor of type resource.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StopGradient Stops gradient computation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StopGradient  
tf.raw_ops.StopGradient(
    input, name=None
)
 When executed in a graph, this op outputs its input tensor as-is. When building ops to compute gradients, this op prevents the contribution of its inputs to be taken into account. Normally, the gradient generator adds ops to a graph to compute the derivatives of a specified 'loss' by recursively finding out inputs that contributed to its computation. If you insert this op in the graph it inputs are masked from the gradient generator. They are not taken into account for computing gradients. This is useful any time you want to compute a value with TensorFlow but need to pretend that the value was a constant. Some examples include:  The EM algorithm where the M-step should not involve backpropagation through the output of the E-step. Contrastive divergence training of Boltzmann machines where, when differentiating the energy function, the training must not backpropagate through the graph that generated the samples from the model. Adversarial training, where no backprop should happen through the adversarial example generation process. 
 


 Args
  input   A Tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.StridedSliceAssign Assign value to the sliced l-value reference of ref.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StridedSliceAssign  
tf.raw_ops.StridedSliceAssign(
    ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 The values of value are assigned to the positions in the variable ref that are selected by the slice parameters. The slice parameters begin, end, strides, etc. work exactly as in StridedSlice. NOTE this op currently does not support broadcasting and so value's shape must be exactly the shape produced by the slice of ref.
 


 Args
  ref   A mutable Tensor.  
  begin   A Tensor. Must be one of the following types: int32, int64.  
  end   A Tensor. Must have the same type as begin.  
  strides   A Tensor. Must have the same type as begin.  
  value   A Tensor. Must have the same type as ref.  
  begin_mask   An optional int. Defaults to 0.  
  end_mask   An optional int. Defaults to 0.  
  ellipsis_mask   An optional int. Defaults to 0.  
  new_axis_mask   An optional int. Defaults to 0.  
  shrink_axis_mask   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.StridedSliceGrad Returns the gradient of StridedSlice.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StridedSliceGrad  
tf.raw_ops.StridedSliceGrad(
    shape, begin, end, strides, dy, begin_mask=0, end_mask=0, ellipsis_mask=0,
    new_axis_mask=0, shrink_axis_mask=0, name=None
)
 Since StridedSlice cuts out pieces of its input which is size shape, its gradient will have the same shape (which is passed here as shape). The gradient will be zero in any element that the slice does not select. Arguments are the same as StridedSliceGrad with the exception that dy is the input gradient to be propagated and shape is the shape of StridedSlice's input.
 


 Args
  shape   A Tensor. Must be one of the following types: int32, int64.  
  begin   A Tensor. Must have the same type as shape.  
  end   A Tensor. Must have the same type as shape.  
  strides   A Tensor. Must have the same type as shape.  
  dy   A Tensor.  
  begin_mask   An optional int. Defaults to 0.  
  end_mask   An optional int. Defaults to 0.  
  ellipsis_mask   An optional int. Defaults to 0.  
  new_axis_mask   An optional int. Defaults to 0.  
  shrink_axis_mask   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as dy.
tf.raw_ops.StringFormat Formats a string template using a list of tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringFormat  
tf.raw_ops.StringFormat(
    inputs, template='%s', placeholder='%s', summarize=3,
    name=None
)
 Formats a string template using a list of tensors, pretty-printing tensor summaries.
 


 Args
  inputs   A list of Tensor objects. The list of tensors to format into the placeholder string.  
  template   An optional string. Defaults to "%s". A string, the template to format tensor summaries into.  
  placeholder   An optional string. Defaults to "%s". A string, at each placeholder in the template a subsequent tensor summary will be inserted.  
  summarize   An optional int. Defaults to 3. When formatting the tensor summaries print the first and last summarize entries of each tensor dimension.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StringJoin Joins the strings in the given list of string tensors into one tensor;  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringJoin  
tf.raw_ops.StringJoin(
    inputs, separator='', name=None
)
 with the given separator (default is an empty separator). Examples: 
s = ["hello", "world", "tensorflow"]
tf.strings.join(s, " ")
<tf.Tensor: shape=(), dtype=string, numpy=b'hello world tensorflow'>

 


 Args
  inputs   A list of at least 1 Tensor objects with type string. A list of string tensors. The tensors must all have the same shape, or be scalars. Scalars may be mixed in; these will be broadcast to the shape of non-scalar inputs.  
  separator   An optional string. Defaults to "". string, an optional join separator.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StringLength String lengths of input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringLength  
tf.raw_ops.StringLength(
    input, unit='BYTE', name=None
)
 Computes the length of each string given in the input tensor. 
strings = tf.constant(['Hello','TensorFlow', '\U0001F642'])
tf.strings.length(strings).numpy() # default counts bytes
array([ 5, 10, 4], dtype=int32)
tf.strings.length(strings, unit="UTF8_CHAR").numpy()
array([ 5, 10, 1], dtype=int32)

 


 Args
  input   A Tensor of type string. The strings for which to compute the length for each element.  
  unit   An optional string from: "BYTE", "UTF8_CHAR". Defaults to "BYTE". The unit that is counted to compute string length. One of: "BYTE" (for the number of bytes in each string) or "UTF8_CHAR" (for the number of UTF-8 encoded Unicode code points in each string). Results are undefined if unit=UTF8_CHAR and the input strings do not contain structurally valid UTF-8.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.StringLower Converts all uppercase characters into their respective lowercase replacements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringLower  
tf.raw_ops.StringLower(
    input, encoding='', name=None
)
 Example: 
tf.strings.lower("CamelCase string and ALL CAPS")
<tf.Tensor: shape=(), dtype=string, numpy=b'camelcase string and all caps'>

 


 Args
  input   A Tensor of type string.  
  encoding   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StringNGrams Creates ngrams from ragged string data.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringNGrams  
tf.raw_ops.StringNGrams(
    data, data_splits, separator, ngram_widths, left_pad, right_pad, pad_width,
    preserve_short_sequences, name=None
)
 This op accepts a ragged tensor with 1 ragged dimension containing only strings and outputs a ragged tensor with 1 ragged dimension containing ngrams of that string, joined along the innermost axis.
 


 Args
  data   A Tensor of type string. The values tensor of the ragged string tensor to make ngrams out of. Must be a 1D string tensor.  
  data_splits   A Tensor. Must be one of the following types: int32, int64. The splits tensor of the ragged string tensor to make ngrams out of.  
  separator   A string. The string to append between elements of the token. Use "" for no separator.  
  ngram_widths   A list of ints. The sizes of the ngrams to create.  
  left_pad   A string. The string to use to pad the left side of the ngram sequence. Only used if pad_width != 0.  
  right_pad   A string. The string to use to pad the right side of the ngram sequence. Only used if pad_width != 0.  
  pad_width   An int. The number of padding elements to add to each side of each sequence. Note that padding will never be greater than 'ngram_widths'-1 regardless of this value. If pad_width=-1, then add max(ngram_widths)-1 elements.  
  preserve_short_sequences   A bool.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (ngrams, ngrams_splits).     ngrams   A Tensor of type string.  
  ngrams_splits   A Tensor. Has the same type as data_splits.
tf.raw_ops.StringSplit Split elements of input based on delimiter into a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringSplit  
tf.raw_ops.StringSplit(
    input, delimiter, skip_empty=True, name=None
)
 Let N be the size of source (typically N will be the batch size). Split each element of input based on delimiter and return a SparseTensor containing the splitted tokens. Empty tokens are ignored. delimiter can be empty, or a string of split characters. If delimiter is an empty string, each element of input is split into individual single-byte character strings, including splitting of UTF-8 multibyte sequences. Otherwise every character of delimiter is a potential split point. For example: N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output will be indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] shape = [2, 3] values = ['hello', 'world', 'a', 'b', 'c']
 


 Args
  input   A Tensor of type string. 1-D. Strings to split.  
  delimiter   A Tensor of type string. 0-D. Delimiter characters (bytes), or empty string.  
  skip_empty   An optional bool. Defaults to True. A bool. If True, skip the empty strings from the result.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (indices, values, shape).     indices   A Tensor of type int64.  
  values   A Tensor of type string.  
  shape   A Tensor of type int64.
tf.raw_ops.StringSplitV2 Split elements of source based on sep into a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringSplitV2  
tf.raw_ops.StringSplitV2(
    input, sep, maxsplit=-1, name=None
)
 Let N be the size of source (typically N will be the batch size). Split each element of source based on sep and return a SparseTensor containing the split tokens. Empty tokens are ignored. For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output will be st.indices = [0, 0;
              0, 1;
              1, 0;
              1, 1;
              1, 2]
st.shape = [2, 3]
st.values = ['hello', 'world', 'a', 'b', 'c']
 If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings. For example, source of "1<>2<><>3" and sep of "<>" returns ["1", "2", "", "3"]. If sep is None or an empty string, consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the startor end if the string has leading or trailing whitespace. Note that the above mentioned behavior matches python's str.split.
 


 Args
  input   A Tensor of type string. 1-D string Tensor, the strings to split.  
  sep   A Tensor of type string. 0-D string Tensor, the delimiter character.  
  maxsplit   An optional int. Defaults to -1. An int. If maxsplit > 0, limit of the split of the result.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (indices, values, shape).     indices   A Tensor of type int64.  
  values   A Tensor of type string.  
  shape   A Tensor of type int64.
tf.raw_ops.StringStrip Strip leading and trailing whitespaces from the Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringStrip  
tf.raw_ops.StringStrip(
    input, name=None
)

 


 Args
  input   A Tensor of type string. A string Tensor of any shape.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.StringToHashBucket Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucket  
tf.raw_ops.StringToHashBucket(
    string_tensor, num_buckets, name=None
)
 The hash function is deterministic on the content of the string within the process. Note that the hash function may change from time to time. This functionality will be deprecated and it's recommended to use tf.string_to_hash_bucket_fast() or tf.string_to_hash_bucket_strong().
 


 Args
  string_tensor   A Tensor of type string.  
  num_buckets   An int that is >= 1. The number of buckets.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.StringToHashBucketFast Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucketFast  
tf.raw_ops.StringToHashBucketFast(
    input, num_buckets, name=None
)
 The hash function is deterministic on the content of the string within the process and will never change. However, it is not suitable for cryptography. This function may be used when CPU time is scarce and inputs are trusted or unimportant. There is a risk of adversaries constructing inputs that all hash to the same bucket. To prevent this problem, use a strong hash function with tf.string_to_hash_bucket_strong. Examples: 
tf.strings.to_hash_bucket_fast(["Hello", "TensorFlow", "2.x"], 3).numpy()
array([0, 2, 2])

 


 Args
  input   A Tensor of type string. The strings to assign a hash bucket.  
  num_buckets   An int that is >= 1. The number of buckets.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.StringToHashBucketStrong Converts each string in the input Tensor to its hash mod by a number of buckets.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringToHashBucketStrong  
tf.raw_ops.StringToHashBucketStrong(
    input, num_buckets, key, name=None
)
 The hash function is deterministic on the content of the string within the process. The hash function is a keyed hash function, where attribute key defines the key of the hash function. key is an array of 2 elements. A strong hash is important when inputs may be malicious, e.g. URLs with additional components. Adversaries could try to make their inputs hash to the same bucket for a denial-of-service attack or to skew the results. A strong hash can be used to make it difficult to find inputs with a skewed hash value distribution over buckets. This requires that the hash function is seeded by a high-entropy (random) "key" unknown to the adversary. The additional robustness comes at a cost of roughly 4x higher compute time than tf.string_to_hash_bucket_fast. Examples: 
tf.strings.to_hash_bucket_strong(["Hello", "TF"], 3, [1, 2]).numpy()
array([2, 0])

 


 Args
  input   A Tensor of type string. The strings to assign a hash bucket.  
  num_buckets   An int that is >= 1. The number of buckets.  
  key   A list of ints. The key used to seed the hash function, passed as a list of two uint64 elements.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.StringToNumber Converts each string in the input Tensor to the specified numeric type.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringToNumber  
tf.raw_ops.StringToNumber(
    string_tensor, out_type=tf.dtypes.float32, name=None
)
 (Note that int32 overflow results in an error while float overflow results in a rounded value.) Example: 
strings = ["5.0", "3.0", "7.0"]
tf.strings.to_number(strings)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([5., 3., 7.], dtype=float32)>

 


 Args
  string_tensor   A Tensor of type string.  
  out_type   An optional tf.DType from: tf.float32, tf.float64, tf.int32, tf.int64. Defaults to tf.float32. The numeric type to interpret each string in string_tensor as.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.StringUpper Converts all lowercase characters into their respective uppercase replacements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.StringUpper  
tf.raw_ops.StringUpper(
    input, encoding='', name=None
)
 Example: 
tf.strings.upper("CamelCase string and ALL CAPS")
<tf.Tensor: shape=(), dtype=string, numpy=b'CAMELCASE STRING AND ALL CAPS'>

 


 Args
  input   A Tensor of type string.  
  encoding   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.