neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ArgMin Returns the index with the smallest value across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ArgMin  
tf.raw_ops.ArgMin(
    input, dimension, output_type=tf.dtypes.int64, name=None
)
 Note that in case of ties the identity of the return value is not guaranteed. Usage: import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmin(input = a)
c = tf.keras.backend.eval(b)
# c = 0
# here a[0] = 1 which is the smallest element of a across axis 0

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool.  
  dimension   A Tensor. Must be one of the following types: int32, int64. int32 or int64, must be in the range [-rank(input), rank(input)). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0.  
  output_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type output_type.
tf.raw_ops.Asin Computes the trignometric inverse sine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Asin  
tf.raw_ops.Asin(
    x, name=None
)
 The tf.math.asin operation returns the inverse of tf.math.sin, such that if y = tf.math.sin(x) then, x = tf.math.asin(y). 
Note: The output of tf.math.asin will lie within the invertible range of sine, i.e [-pi/2, pi/2].
 For example: # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]
x = tf.constant([1.047, 0.785])
y = tf.math.sin(x) # [0.8659266, 0.7068252]

tf.math.asin(y) # [1.047, 0.785] = x

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Asinh Computes inverse hyperbolic sine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Asinh  
tf.raw_ops.Asinh(
    x, name=None
)
 Given an input tensor, this function computes inverse hyperbolic sine for every element in the tensor. Both input and output has a range of [-inf, inf]. x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 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.Assert Asserts that the given condition is true.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Assert  
tf.raw_ops.Assert(
    condition, data, summarize=3, name=None
)
 If condition evaluates to false, print the list of tensors in data. summarize determines how many entries of the tensors to print.
 


 Args
  condition   A Tensor of type bool. The condition to evaluate.  
  data   A list of Tensor objects. The tensors to print out when condition is false.  
  summarize   An optional int. Defaults to 3. Print this many entries of each tensor.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.AssertCardinalityDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssertCardinalityDataset  
tf.raw_ops.AssertCardinalityDataset(
    input_dataset, cardinality, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  cardinality   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.AssertNextDataset A transformation that asserts which transformations happen next.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssertNextDataset  
tf.raw_ops.AssertNextDataset(
    input_dataset, transformations, output_types, output_shapes, name=None
)
 This transformation checks whether the camel-case names (i.e. "FlatMap", not "flat_map") of the transformations following this transformation match the list of names in the transformations argument. If there is a mismatch, the transformation raises an exception. The check occurs when iterating over the contents of the dataset, which means that the check happens after any static optimizations are applied to the dataset graph.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset. AssertNextDataset passes through the outputs of its input dataset.  
  transformations   A Tensor of type string. A tf.string vector tf.Tensor identifying the transformations that are expected to happen next.  
  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.Assign Update 'ref' by assigning 'value' to it.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Assign  
tf.raw_ops.Assign(
    ref, value, validate_shape=True, use_locking=True, name=None
)
 This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value.
 


 Args
  ref   A mutable Tensor. Should be from a Variable node. May be uninitialized.  
  value   A Tensor. Must have the same type as ref. The value to be assigned to the variable.  
  validate_shape   An optional bool. Defaults to True. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'.  
  use_locking   An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.AssignAdd Update 'ref' by adding 'value' to it.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssignAdd  
tf.raw_ops.AssignAdd(
    ref, value, use_locking=False, name=None
)
 This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  value   A Tensor. Must have the same type as ref. The value to be added to the variable.  
  use_locking   An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.AssignAddVariableOp Adds a value to the current value of a variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssignAddVariableOp  
tf.raw_ops.AssignAddVariableOp(
    resource, value, name=None
)
 Any ReadVariableOp with a control dependency on this op is guaranteed to see the incremented value or a subsequent newer one.
 


 Args
  resource   A Tensor of type resource. handle to the resource in which to store the variable.  
  value   A Tensor. the value by which the variable will be incremented.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.AssignSub Update 'ref' by subtracting 'value' from it.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssignSub  
tf.raw_ops.AssignSub(
    ref, value, use_locking=False, name=None
)
 This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  value   A Tensor. Must have the same type as ref. The value to be subtracted to the variable.  
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.AssignSubVariableOp Subtracts a value from the current value of a variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssignSubVariableOp  
tf.raw_ops.AssignSubVariableOp(
    resource, value, name=None
)
 Any ReadVariableOp with a control dependency on this op is guaranteed to see the decremented value or a subsequent newer one.
 


 Args
  resource   A Tensor of type resource. handle to the resource in which to store the variable.  
  value   A Tensor. the value by which the variable will be incremented.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.AssignVariableOp Assigns a new value to a variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AssignVariableOp  
tf.raw_ops.AssignVariableOp(
    resource, value, name=None
)
 Any ReadVariableOp with a control dependency on this op is guaranteed to return this value or a subsequent newer value of the variable.
 


 Args
  resource   A Tensor of type resource. handle to the resource in which to store the variable.  
  value   A Tensor. the value to set the new tensor to use.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.AsString Converts each entry in the given tensor to strings.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AsString  
tf.raw_ops.AsString(
    input, precision=-1, scientific=False, shortest=False, width=-1,
    fill='', name=None
)
 Supports many numeric types and boolean. For Unicode, see the https://www.tensorflow.org/tutorials/representation/unicode tutorial. Examples: 
tf.strings.as_string([3, 2])
<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'3', b'2'], dtype=object)>
tf.strings.as_string([3.1415926, 2.71828], precision=2).numpy()
array([b'3.14', b'2.72'], dtype=object)

 


 Args
  input   A Tensor. Must be one of the following types: int8, int16, int32, int64, complex64, complex128, float32, float64, bool.  
  precision   An optional int. Defaults to -1. The post-decimal precision to use for floating point numbers. Only used if precision > -1.  
  scientific   An optional bool. Defaults to False. Use scientific notation for floating point numbers.  
  shortest   An optional bool. Defaults to False. Use shortest representation (either scientific or standard) for floating point numbers.  
  width   An optional int. Defaults to -1. Pad pre-decimal numbers to this width. Applies to both floating point and integer numbers. Only used if width > -1.  
  fill   An optional string. Defaults to "". The value to pad if width > -1. If empty, pads with spaces. Another typical value is '0'. String cannot be longer than 1 character.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.Atan Computes the trignometric inverse tangent of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Atan  
tf.raw_ops.Atan(
    x, name=None
)
 The tf.math.atan operation returns the inverse of tf.math.tan, such that if y = tf.math.tan(x) then, x = tf.math.atan(y). 
Note: The output of tf.math.atan will lie within the invertible range of tan, i.e (-pi/2, pi/2).
 For example: # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]
x = tf.constant([1.047, 0.785])
y = tf.math.tan(x) # [1.731261, 0.99920404]

tf.math.atan(y) # [1.047, 0.785] = x

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Atan2   Computes arctangent of y/x element-wise, respecting signs of the arguments.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Atan2  
tf.raw_ops.Atan2(
    y, x, name=None
)
 This is the angle ( \theta \in [-\pi, \pi] ) such that [ x = r \cos(\theta) ] and [ y = r \sin(\theta) ] where (r = \sqrt(x^2 + y^2) ).
 


 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  x   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.Atanh Computes inverse hyperbolic tangent of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Atanh  
tf.raw_ops.Atanh(
    x, name=None
)
 Given an input tensor, this function computes inverse hyperbolic tangent for every element in the tensor. Input range is [-1,1] and output range is [-inf, inf]. If input is -1, output will be -inf and if the input is 1, output will be inf. Values outside the range will have nan as output. x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")])
tf.math.atanh(x) ==> [nan -inf -0.54930615 inf  0. 0.54930615 nan 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.AudioSpectrogram Produces a visualization of audio data over time.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AudioSpectrogram  
tf.raw_ops.AudioSpectrogram(
    input, window_size, stride, magnitude_squared=False, name=None
)
 Spectrograms are a standard way of representing audio information as a series of slices of frequency information, one slice for each window of time. By joining these together into a sequence, they form a distinctive fingerprint of the sound over time. This op expects to receive audio data as an input, stored as floats in the range -1 to 1, together with a window width in samples, and a stride specifying how far to move the window between slices. From this it generates a three dimensional output. The first dimension is for the channels in the input, so a stereo audio input would have two here for example. The second dimension is time, with successive frequency slices. The third dimension has an amplitude value for each frequency during that time slice. This means the layout when converted and saved as an image is rotated 90 degrees clockwise from a typical spectrogram. Time is descending down the Y axis, and the frequency decreases from left to right. Each value in the result represents the square root of the sum of the real and imaginary parts of an FFT on the current window of samples. In this way, the lowest dimension represents the power of each frequency in the current window, and adjacent windows are concatenated in the next dimension. To get a more intuitive and visual look at what this operation does, you can run tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the resulting spectrogram as a PNG image.
 


 Args
  input   A Tensor of type float32. Float representation of audio data.  
  window_size   An int. How wide the input window is in samples. For the highest efficiency this should be a power of two, but other values are accepted.  
  stride   An int. How widely apart the center of adjacent sample windows should be.  
  magnitude_squared   An optional bool. Defaults to False. Whether to return the squared magnitude or just the magnitude. Using squared magnitude can avoid extra calculations.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.AudioSummary Outputs a Summary protocol buffer with audio.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AudioSummary  
tf.raw_ops.AudioSummary(
    tag, tensor, sample_rate, max_outputs=3, name=None
)
 The summary has up to max_outputs summary values containing audio. The audio is built from tensor which must be 3-D with shape [batch_size, frames, channels] or 2-D with shape [batch_size, frames]. The values are assumed to be in the range of [-1.0, 1.0] with a sample rate of sample_rate. The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values:  If max_outputs is 1, the summary value tag is 'tag/audio'. If max_outputs is greater than 1, the summary value tags are generated sequentially as 'tag/audio/0', 'tag/audio/1', etc. 
 


 Args
  tag   A Tensor of type string. Scalar. Used to build the tag attribute of the summary values.  
  tensor   A Tensor of type float32. 2-D of shape [batch_size, frames].  
  sample_rate   A float. The sample rate of the signal in hertz.  
  max_outputs   An optional int that is >= 1. Defaults to 3. Max number of batch elements to generate audio for.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.AudioSummaryV2 Outputs a Summary protocol buffer with audio.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AudioSummaryV2  
tf.raw_ops.AudioSummaryV2(
    tag, tensor, sample_rate, max_outputs=3, name=None
)
 The summary has up to max_outputs summary values containing audio. The audio is built from tensor which must be 3-D with shape [batch_size, frames, channels] or 2-D with shape [batch_size, frames]. The values are assumed to be in the range of [-1.0, 1.0] with a sample rate of sample_rate. The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values:  If max_outputs is 1, the summary value tag is 'tag/audio'. If max_outputs is greater than 1, the summary value tags are generated sequentially as 'tag/audio/0', 'tag/audio/1', etc. 
 


 Args
  tag   A Tensor of type string. Scalar. Used to build the tag attribute of the summary values.  
  tensor   A Tensor of type float32. 2-D of shape [batch_size, frames].  
  sample_rate   A Tensor of type float32. The sample rate of the signal in hertz.  
  max_outputs   An optional int that is >= 1. Defaults to 3. Max number of batch elements to generate audio for.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.AutoShardDataset Creates a dataset that shards the input dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AutoShardDataset  
tf.raw_ops.AutoShardDataset(
    input_dataset, num_workers, index, output_types, output_shapes,
    auto_shard_policy=0, num_replicas=0, name=None
)
 Creates a dataset that shards the input dataset by num_workers, returning a sharded dataset for the index-th worker. This attempts to automatically shard a dataset by examining the Dataset graph and inserting a shard op before the inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset). This dataset will throw a NotFound error if we cannot shard the dataset automatically.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  num_workers   A Tensor of type int64. A scalar representing the number of workers to distribute this dataset across.  
  index   A Tensor of type int64. A scalar representing the index of the current worker out of num_workers.  
  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.  
  auto_shard_policy   An optional int. Defaults to 0.  
  num_replicas   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.AvgPool Performs average pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AvgPool  
tf.raw_ops.AvgPool(
    value, ksize, strides, padding, data_format='NHWC', name=None
)
 Each entry in output is the mean of the corresponding size ksize window in value.
 


 Args
  value   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, height, width, channels].  
  ksize   A list of ints that has length >= 4. The size of the sliding window for each dimension of value.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of value.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as value.
tf.raw_ops.AvgPool3D Performs 3D average pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.AvgPool3D  
tf.raw_ops.AvgPool3D(
    input, ksize, strides, padding, data_format='NDHWC', name=None
)
 Each entry in output is the mean of the corresponding size ksize window in value.
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, depth, rows, cols, channels] tensor to pool over.  
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.