neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ExperimentalSqlDataset Creates a dataset that executes a SQL query and emits rows of the result set.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalSqlDataset  
tf.raw_ops.ExperimentalSqlDataset(
    driver_name, data_source_name, query, output_types, output_shapes, name=None
)

 


 Args
  driver_name   A Tensor of type string. The database type. Currently, the only supported type is 'sqlite'.  
  data_source_name   A Tensor of type string. A connection string to connect to the database.  
  query   A Tensor of type string. A SQL query to execute.  
  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.ExperimentalStatsAggregatorHandle Creates a statistics manager resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalStatsAggregatorHandle  
tf.raw_ops.ExperimentalStatsAggregatorHandle(
    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.ExperimentalStatsAggregatorSummary 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.ExperimentalStatsAggregatorSummary  
tf.raw_ops.ExperimentalStatsAggregatorSummary(
    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.ExperimentalTakeWhileDataset Creates a dataset that stops iteration when predicate` is false.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalTakeWhileDataset  
tf.raw_ops.ExperimentalTakeWhileDataset(
    input_dataset, other_arguments, predicate, output_types, output_shapes,
    name=None
)
 The predicate function must return a scalar boolean and accept the following arguments:  One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments. 
 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.  
  predicate   A function decorated with @Defun. A function returning a scalar boolean.  
  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.ExperimentalThreadPoolDataset Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalThreadPoolDataset  
tf.raw_ops.ExperimentalThreadPoolDataset(
    input_dataset, thread_pool, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  thread_pool   A Tensor of type resource. A resource produced by the ThreadPoolHandle op.  
  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.ExperimentalThreadPoolHandle Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalThreadPoolHandle  
tf.raw_ops.ExperimentalThreadPoolHandle(
    num_threads, display_name, max_intra_op_parallelism=1, container='',
    shared_name='', name=None
)

 


 Args
  num_threads   An int. The number of threads in the thread pool.  
  display_name   A string. A human-readable name for the threads that may be visible in some visualizations. threadpool.  
  max_intra_op_parallelism   An optional int. Defaults to 1. The maximum degree of parallelism to use within operations that execute on this threadpool.  
  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.ExperimentalUnbatchDataset A dataset that splits the elements of its input into multiple elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUnbatchDataset  
tf.raw_ops.ExperimentalUnbatchDataset(
    input_dataset, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  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.ExperimentalUniqueDataset Creates a dataset that contains the unique elements of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUniqueDataset  
tf.raw_ops.ExperimentalUniqueDataset(
    input_dataset, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  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.Expint  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Expint  
tf.raw_ops.Expint(
    x, name=None
)

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Expm1 Computes exp(x) - 1 element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Expm1  
tf.raw_ops.Expm1(
    x, name=None
)
 i.e. exp(x) - 1 or e^(x) - 1, where x is the input tensor. e denotes Euler's number and is approximately equal to 2.718281. x = tf.constant(2.0)
tf.math.expm1(x) ==> 6.389056

x = tf.constant([2.0, 8.0])
tf.math.expm1(x) ==> array([6.389056, 2979.958], dtype=float32)

x = tf.constant(1 + 1j)
tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j)

 


 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.ExtractGlimpse Extracts a glimpse from the input tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpse  
tf.raw_ops.ExtractGlimpse(
    input, size, offsets, centered=True, normalized=True, uniform_noise=True,
    noise='uniform', name=None
)
 Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built:  If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels. 
 


 Args
  input   A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].  
  size   A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.  
  offsets   A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.  
  centered   An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.  
  normalized   An optional bool. Defaults to True. indicates if the offset coordinates are normalized.  
  uniform_noise   An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.  
  noise   An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ExtractGlimpseV2 Extracts a glimpse from the input tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpseV2  
tf.raw_ops.ExtractGlimpseV2(
    input, size, offsets, centered=True, normalized=True, uniform_noise=True,
    noise='uniform', name=None
)
 Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built:  If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels. 
 


 Args
  input   A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].  
  size   A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.  
  offsets   A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.  
  centered   An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.  
  normalized   An optional bool. Defaults to True. indicates if the offset coordinates are normalized.  
  uniform_noise   An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.  
  noise   An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ExtractImagePatches Extract patches from images and put them in the "depth" output dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractImagePatches  
tf.raw_ops.ExtractImagePatches(
    images, ksizes, strides, rates, padding, name=None
)

 


 Args
  images   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, uint8, uint16, uint32, uint64, complex64, complex128, bool. 4-D Tensor with shape [batch, in_rows, in_cols, depth].  
  ksizes   A list of ints that has length >= 4. The size of the sliding window for each dimension of images.  
  strides   A list of ints that has length >= 4. How far the centers of two consecutive patches are in the images. Must be: [1, stride_rows, stride_cols, 1].  
  rates   A list of ints that has length >= 4. Must be: [1, rate_rows, rate_cols, 1]. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by subsampling them spatially by a factor of rates. This is equivalent to rate in dilated (a.k.a. Atrous) convolutions.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.ExtractJpegShape Extract the shape information of a JPEG-encoded image.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractJpegShape  
tf.raw_ops.ExtractJpegShape(
    contents, output_type=tf.dtypes.int32, name=None
)
 This op only parses the image header, so it is much faster than DecodeJpeg.
 


 Args
  contents   A Tensor of type string. 0-D. The JPEG-encoded image.  
  output_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32. (Optional) The output type of the operation (int32 or int64). Defaults to int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type output_type.
tf.raw_ops.ExtractVolumePatches Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractVolumePatches  
tf.raw_ops.ExtractVolumePatches(
    input, ksizes, strides, padding, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 5-D Tensor with shape [batch, in_planes, in_rows, in_cols, depth].  
  ksizes   A list of ints that has length >= 5. The size of the sliding window for each dimension of input.  
  strides   A list of ints that has length >= 5. 1-D of length 5. How far the centers of two consecutive patches are in input. Must be: [1, stride_planes, stride_rows, stride_cols, 1].  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use. The size-related attributes are specified as follows: ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
strides = [1, stride_planes, strides_rows, strides_cols, 1]

 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Fact Output a fact about factorials.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Fact  
tf.raw_ops.Fact(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.FakeParam This op is used as a placeholder in If branch functions. It doesn't provide a  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeParam  
tf.raw_ops.FakeParam(
    dtype, shape, name=None
)
 valid output when run, so must either be removed (e.g. replaced with a function input) or guaranteed not to be used (e.g. if mirroring an intermediate output needed for the gradient computation of the other branch).
 


 Args
  dtype   A tf.DType. The type of the output.  
  shape   A tf.TensorShape or list of ints. The purported shape of the output. This is only used for shape inference; the output will not necessarily have this shape. Can be a partial shape.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgs  
tf.raw_ops.FakeQuantWithMinMaxArgs(
    inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)
 Attributes  
[min; max] define the clamping range for the inputs data. 
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval. 
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  Quantization is called fake since the output is still in floating point.
 


 Args
  inputs   A Tensor of type float32.  
  min   An optional float. Defaults to -6.  
  max   An optional float. Defaults to 6.  
  num_bits   An optional int. Defaults to 8.  
  narrow_range   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgsGradient  
tf.raw_ops.FakeQuantWithMinMaxArgsGradient(
    gradients, inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)

 


 Args
  gradients   A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.  
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxArgs operation.  
  min   An optional float. Defaults to -6.  
  max   An optional float. Defaults to 6.  
  num_bits   An optional int. Defaults to 8.  
  narrow_range   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVars  
tf.raw_ops.FakeQuantWithMinMaxVars(
    inputs, min, max, num_bits=8, narrow_range=False, name=None
)
 Fake-quantize the inputs tensor of type float via global float scalars min and max to outputs tensor of same shape as inputs. Attributes  
[min; max] define the clamping range for the inputs data. 
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval. 
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  This operation has a gradient and thus allows for training min and max values.
 


 Args
  inputs   A Tensor of type float32.  
  min   A Tensor of type float32.  
  max   A Tensor of type float32.  
  num_bits   An optional int. Defaults to 8.  
  narrow_range   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsGradient  
tf.raw_ops.FakeQuantWithMinMaxVarsGradient(
    gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)

 


 Args
  gradients   A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation.  
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation. min, max: Quantization interval, scalar floats.  
  min   A Tensor of type float32.  
  max   A Tensor of type float32.  
  num_bits   An optional int. Defaults to 8. The bitwidth of the quantization; between 2 and 8, inclusive.  
  narrow_range   An optional bool. Defaults to False. Whether to quantize into 2^num_bits - 1 distinct values.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).     backprops_wrt_input   A Tensor of type float32.  
  backprop_wrt_min   A Tensor of type float32.  
  backprop_wrt_max   A Tensor of type float32.
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float via per-channel floats  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannel  
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(
    inputs, min, max, num_bits=8, narrow_range=False, name=None
)
 Fake-quantize the inputs tensor of type float per-channel and one of the shapes: [d], [b, d] [b, h, w, d] via per-channel floats min and max of shape [d] to outputs tensor of same shape as inputs. Attributes  
[min; max] define the clamping range for the inputs data. 
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval. 
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  This operation has a gradient and thus allows for training min and max values.
 


 Args
  inputs   A Tensor of type float32.  
  min   A Tensor of type float32.  
  max   A Tensor of type float32.  
  num_bits   An optional int. Defaults to 8.  
  narrow_range   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.