neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ShuffleAndRepeatDataset Creates a dataset that shuffles and repeats elements from input_dataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleAndRepeatDataset  
tf.raw_ops.ShuffleAndRepeatDataset(
    input_dataset, buffer_size, seed, seed2, count, output_types, output_shapes,
    reshuffle_each_iteration=True, name=None
)
 pseudorandomly.
 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64. The number of output elements to buffer in an iterator over this dataset. Compare with the min_after_dequeue attr when creating a RandomShuffleQueue.  
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.  
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.  
  count   A Tensor of type int64. A scalar representing the number of times the underlying dataset should be repeated. The default is -1, which results in infinite repetition.  
  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.  
  reshuffle_each_iteration   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ShuffleAndRepeatDatasetV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleAndRepeatDatasetV2  
tf.raw_ops.ShuffleAndRepeatDatasetV2(
    input_dataset, buffer_size, seed, seed2, count, seed_generator, output_types,
    output_shapes, reshuffle_each_iteration=True, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64.  
  seed   A Tensor of type int64.  
  seed2   A Tensor of type int64.  
  count   A Tensor of type int64.  
  seed_generator   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.  
  reshuffle_each_iteration   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ShuffleDataset Creates a dataset that shuffles elements from input_dataset pseudorandomly.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDataset  
tf.raw_ops.ShuffleDataset(
    input_dataset, buffer_size, seed, seed2, output_types, output_shapes,
    reshuffle_each_iteration=True, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64. The number of output elements to buffer in an iterator over this dataset. Compare with the min_after_dequeue attr when creating a RandomShuffleQueue.  
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.  
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.  
  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.  
  reshuffle_each_iteration   An optional bool. Defaults to True. If true, each iterator over this dataset will be given a different pseudorandomly generated seed, based on a sequence seeded by the seed and seed2 inputs. If false, each iterator will be given the same seed, and repeated iteration over this dataset will yield the exact same sequence of results.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ShuffleDatasetV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDatasetV2  
tf.raw_ops.ShuffleDatasetV2(
    input_dataset, buffer_size, seed_generator, output_types, output_shapes,
    name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64.  
  seed_generator   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.ShuffleDatasetV3  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShuffleDatasetV3  
tf.raw_ops.ShuffleDatasetV3(
    input_dataset, buffer_size, seed, seed2, seed_generator, output_types,
    output_shapes, reshuffle_each_iteration=True, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  buffer_size   A Tensor of type int64.  
  seed   A Tensor of type int64.  
  seed2   A Tensor of type int64.  
  seed_generator   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.  
  reshuffle_each_iteration   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ShutdownDistributedTPU Shuts down a running distributed TPU system.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ShutdownDistributedTPU  
tf.raw_ops.ShutdownDistributedTPU(
    name=None
)
 The op returns an error if no system is running.
 


 Args
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Sigmoid Computes sigmoid of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sigmoid  
tf.raw_ops.Sigmoid(
    x, name=None
)
 Specifically, y = 1 / (1 + exp(-x)).
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.SigmoidGrad Computes the gradient of the sigmoid of x wrt its input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SigmoidGrad  
tf.raw_ops.SigmoidGrad(
    y, dy, name=None
)
 Specifically, grad = dy * y * (1 - y), where y = sigmoid(x), and dy is the corresponding input gradient.
 


 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  dy   A Tensor. Must have the same type as y.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as y.
tf.raw_ops.Sign Returns an element-wise indication of the sign of a number.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sign  
tf.raw_ops.Sign(
    x, name=None
)
 y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0. For complex numbers, y = sign(x) = x / |x| if x != 0, otherwise y = 0. Example usage: 
tf.math.sign([0., 2., -3.])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.,  1., -1.], dtype=float32)>

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Sin Computes sine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sin  
tf.raw_ops.Sin(
    x, name=None
)
 Given an input tensor, this function computes sine of every element in the tensor. Input range is (-inf, inf) and output range is [-1,1]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])
tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Sinh Computes hyperbolic sine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Sinh  
tf.raw_ops.Sinh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic sine of every element in the tensor. Input range is [-inf,inf] and output range is [-inf,inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Size Returns the size of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Size  
tf.raw_ops.Size(
    input, out_type=tf.dtypes.int32, name=None
)
 This operation returns an integer representing the number of elements in input. For example: # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12

 


 Args
  input   A Tensor.  
  out_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type out_type.
tf.raw_ops.SkipDataset Creates a dataset that skips count elements from the input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SkipDataset  
tf.raw_ops.SkipDataset(
    input_dataset, count, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  count   A Tensor of type int64. A scalar representing the number of elements from the input_dataset that should be skipped. If count is -1, skips everything.  
  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.SleepDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SleepDataset  
tf.raw_ops.SleepDataset(
    input_dataset, sleep_microseconds, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  sleep_microseconds   A Tensor of type int64.  
  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.Slice Return a slice from 'input'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Slice  
tf.raw_ops.Slice(
    input, begin, size, name=None
)
 The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'. Requirements: 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
 


 Args
  input   A Tensor.  
  begin   A Tensor. Must be one of the following types: int32, int64. begin[i] specifies the offset into the 'i'th dimension of 'input' to slice from.  
  size   A Tensor. Must have the same type as begin. size[i] specifies the number of elements of the 'i'th dimension of 'input' to slice. If size[i] is -1, all remaining elements in dimension i are included in the slice (i.e. this is equivalent to setting size[i] = input.dim_size(i) - begin[i]).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.SlidingWindowDataset Creates a dataset that passes a sliding window over input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SlidingWindowDataset  
tf.raw_ops.SlidingWindowDataset(
    input_dataset, window_size, window_shift, window_stride, output_types,
    output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  window_size   A Tensor of type int64. A scalar representing the number of elements in the sliding window.  
  window_shift   A Tensor of type int64. A scalar representing the steps moving the sliding window forward in one iteration. It must be positive.  
  window_stride   A Tensor of type int64. A scalar representing the stride of the input elements of the sliding window. It must be positive.  
  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.Snapshot Returns a copy of the input tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Snapshot  
tf.raw_ops.Snapshot(
    input, name=None
)

 


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


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.SnapshotDataset Creates a dataset that will write to / read from a snapshot.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SnapshotDataset  
tf.raw_ops.SnapshotDataset(
    input_dataset, path, output_types, output_shapes, compression='',
    reader_path_prefix='', writer_path_prefix='',
    shard_size_bytes=10737418240, pending_snapshot_expiry_seconds=86400,
    num_reader_threads=1, reader_buffer_size=1, num_writer_threads=1,
    writer_buffer_size=1, shuffle_on_read=False, seed=0, seed2=0,
    mode='auto', snapshot_name='', name=None
)
 This dataset attempts to determine whether a valid snapshot exists at the snapshot_path, and reads from the snapshot in lieu of using input_dataset. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  path   A Tensor of type string. The path we should write snapshots to / read snapshots from.  
  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.  
  compression   An optional string. Defaults to "".  
  reader_path_prefix   An optional string. Defaults to "".  
  writer_path_prefix   An optional string. Defaults to "".  
  shard_size_bytes   An optional int. Defaults to 10737418240.  
  pending_snapshot_expiry_seconds   An optional int. Defaults to 86400.  
  num_reader_threads   An optional int. Defaults to 1.  
  reader_buffer_size   An optional int. Defaults to 1.  
  num_writer_threads   An optional int. Defaults to 1.  
  writer_buffer_size   An optional int. Defaults to 1.  
  shuffle_on_read   An optional bool. Defaults to False.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  mode   An optional string. Defaults to "auto".  
  snapshot_name   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.SnapshotDatasetV2 Creates a dataset that will write to / read from a snapshot.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SnapshotDatasetV2  
tf.raw_ops.SnapshotDatasetV2(
    input_dataset, path, reader_func_other_args, shard_func_other_args,
    output_types, output_shapes, reader_func, shard_func, compression='',
    reader_prefix='', writer_prefix='', name=None
)
 This dataset attempts to determine whether a valid snapshot exists at the snapshot_path, and reads from the snapshot in lieu of using input_dataset. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  path   A Tensor of type string. The path we should write snapshots to / read snapshots from.  
  reader_func_other_args   A list of Tensor objects.  
  shard_func_other_args   A list of Tensor objects.  
  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.  
  reader_func   A function decorated with @Defun. Optional. A function to control how to read data from snapshot shards.  
  shard_func   A function decorated with @Defun. Optional. A function to control how to shard data when writing a snapshot.  
  compression   An optional string. Defaults to "". The type of compression to be applied to the saved snapshot files.  
  reader_prefix   An optional string. Defaults to "".  
  writer_prefix   An optional string. Defaults to "".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.SobolSample Generates points from the Sobol sequence.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SobolSample  
tf.raw_ops.SobolSample(
    dim, num_results, skip, dtype=tf.dtypes.float32, name=None
)
 Creates a Sobol sequence with num_results samples. Each sample has dimension dim. Skips the first skip samples.
 


 Args
  dim   A Tensor of type int32. Positive scalar Tensor representing each sample's dimension.  
  num_results   A Tensor of type int32. Positive scalar Tensor of dtype int32. The number of Sobol points to return in the output.  
  skip   A Tensor of type int32. Positive scalar Tensor of dtype int32. The number of initial points of the Sobol sequence to skip.  
  dtype   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32. The type of the sample. One of: float32 or float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.Softmax   Computes softmax activations.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Softmax  
tf.raw_ops.Softmax(
    logits, name=None
)
 For each batch i and class j we have  $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ 
 


 Args
  logits   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 2-D with shape [batch_size, num_classes].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as logits.
tf.raw_ops.SoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SoftmaxCrossEntropyWithLogits  
tf.raw_ops.SoftmaxCrossEntropyWithLogits(
    features, labels, name=None
)
 Inputs are the logits, not probabilities.
 


 Args
  features   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. batch_size x num_classes matrix  
  labels   A Tensor. Must have the same type as features. batch_size x num_classes matrix The caller must ensure that each batch of labels represents a valid probability distribution.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (loss, backprop).     loss   A Tensor. Has the same type as features.  
  backprop   A Tensor. Has the same type as features.
tf.raw_ops.Softplus Computes softplus: log(exp(features) + 1).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Softplus  
tf.raw_ops.Softplus(
    features, name=None
)

 


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


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.SoftplusGrad Computes softplus gradients for a softplus operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SoftplusGrad  
tf.raw_ops.SoftplusGrad(
    gradients, features, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding softplus operation.  
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding softplus operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.Softsign Computes softsign: features / (abs(features) + 1).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Softsign  
tf.raw_ops.Softsign(
    features, name=None
)

 


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


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.SoftsignGrad Computes softsign gradients for a softsign operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SoftsignGrad  
tf.raw_ops.SoftsignGrad(
    gradients, features, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding softsign operation.  
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding softsign operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.