neulab/docprompting-conala huggingface dataset:
tf.raw_ops.IdentityReaderV2 A Reader that outputs the queued work as both the key and value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IdentityReaderV2  
tf.raw_ops.IdentityReaderV2(
    container='', shared_name='', name=None
)
 To use, enqueue strings in a Queue. ReaderRead will take the front work string and output (work, work).
 


 Args
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.If output = cond ? then_branch(input) : else_branch(input)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.If  
tf.raw_ops.If(
    cond, input, Tout, then_branch, else_branch, output_shapes=[], name=None
)

 


 Args
  cond   A Tensor. A Tensor. If the tensor is a scalar of non-boolean type, 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, being empty means False and being non-empty means True.  
  input   A list of Tensor objects. A list of input tensors.  
  Tout   A list of tf.DTypes. A list of output types.  
  then_branch   A function decorated with @Defun. A function that takes 'inputs' and returns a list of tensors, whose types are the same as what else_branch returns.  
  else_branch   A function decorated with @Defun. A function that takes 'inputs' and returns a list of tensors, whose types are the same as what then_branch returns.  
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.IFFT Inverse fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT  
tf.raw_ops.IFFT(
    input, name=None
)
 Computes the inverse 1-dimensional discrete Fourier transform over the inner-most dimension of input.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.IFFT2D Inverse 2D fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT2D  
tf.raw_ops.IFFT2D(
    input, name=None
)
 Computes the inverse 2-dimensional discrete Fourier transform over the inner-most 2 dimensions of input.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.IFFT3D Inverse 3D fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT3D  
tf.raw_ops.IFFT3D(
    input, name=None
)
 Computes the inverse 3-dimensional discrete Fourier transform over the inner-most 3 dimensions of input.
 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Igamma   Compute the lower regularized incomplete Gamma function P(a, x).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Igamma  
tf.raw_ops.Igamma(
    a, x, name=None
)
 The lower regularized incomplete Gamma function is defined as: \(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\) where \(gamma(a, x) = \\int_{0}^{x} t^{a-1} exp(-t) dt\) is the lower incomplete Gamma function. Note, above Q(a, x) (Igammac) is the upper regularized complete Gamma function.
 


 Args
  a   A Tensor. Must be one of the following types: float32, float64.  
  x   A Tensor. Must have the same type as a.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.Igammac   Compute the upper regularized incomplete Gamma function Q(a, x).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Igammac  
tf.raw_ops.Igammac(
    a, x, name=None
)
 The upper regularized incomplete Gamma function is defined as: \(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\) where \(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\) is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function.
 


 Args
  a   A Tensor. Must be one of the following types: float32, float64.  
  x   A Tensor. Must have the same type as a.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.IgammaGradA Computes the gradient of igamma(a, x) wrt a.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IgammaGradA  
tf.raw_ops.IgammaGradA(
    a, x, name=None
)

 


 Args
  a   A Tensor. Must be one of the following types: float32, float64.  
  x   A Tensor. Must have the same type as a.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.IgnoreErrorsDataset Creates a dataset that contains the elements of input_dataset ignoring errors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IgnoreErrorsDataset  
tf.raw_ops.IgnoreErrorsDataset(
    input_dataset, output_types, output_shapes, log_warning=False, 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.  
  log_warning   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.Imag   Returns the imaginary part of a complex number.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Imag  
tf.raw_ops.Imag(
    input, Tout=tf.dtypes.float32, name=None
)
 Given a tensor input of complex numbers, this operation returns a tensor of type float that is the imaginary part of each element in input. All elements in input must be complex numbers of the form \(a + bj\), where a is the real part and b is the imaginary part returned by this operation. For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.imag(input) ==> [4.75, 5.75]

 


 Args
  input   A Tensor. Must be one of the following types: complex64, complex128.  
  Tout   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tout.
tf.raw_ops.ImageProjectiveTransformV2 Applies the given transform to each of the images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ImageProjectiveTransformV2  
tf.raw_ops.ImageProjectiveTransformV2(
    images, transforms, output_shape, interpolation, fill_mode='CONSTANT',
    name=None
)
 If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. If the transformed point lays outside of the input image, the output pixel is set to 0.
 


 Args
  images   A Tensor. Must be one of the following types: uint8, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels].  
  transforms   A Tensor of type float32. 2-D Tensor, [batch, 8] or [1, 8] matrix, where each row corresponds to a 3 x 3 projective transformation matrix, with the last entry assumed to be 1. If there is one row, the same transformation will be applied to all images.  
  output_shape   A Tensor of type int32. 1-D Tensor [new_height, new_width].  
  interpolation   A string. Interpolation method, "NEAREST" or "BILINEAR".  
  fill_mode   An optional string. Defaults to "CONSTANT". Fill mode, "REFLECT", "WRAP", or "CONSTANT".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.ImageProjectiveTransformV3 Applies the given transform to each of the images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ImageProjectiveTransformV3  
tf.raw_ops.ImageProjectiveTransformV3(
    images, transforms, output_shape, fill_value, interpolation,
    fill_mode='CONSTANT', name=None
)
 If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. If the transformed point lays outside of the input image, the output pixel is set to fill_value.
 


 Args
  images   A Tensor. Must be one of the following types: uint8, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels].  
  transforms   A Tensor of type float32. 2-D Tensor, [batch, 8] or [1, 8] matrix, where each row corresponds to a 3 x 3 projective transformation matrix, with the last entry assumed to be 1. If there is one row, the same transformation will be applied to all images.  
  output_shape   A Tensor of type int32. 1-D Tensor [new_height, new_width].  
  fill_value   A Tensor of type float32. float, the value to be filled when fill_mode is constant".  
  interpolation   A string. Interpolation method, "NEAREST" or "BILINEAR".  
  fill_mode   An optional string. Defaults to "CONSTANT". Fill mode, "REFLECT", "WRAP", or "CONSTANT".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.ImageSummary Outputs a Summary protocol buffer with images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ImageSummary  
tf.raw_ops.ImageSummary(
    tag, tensor, max_images=3, bad_color=_execute.make_tensor(\n    'dtype:
    DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0
    int_val: 255'\n    , 'bad_color'), name=None
)
 The summary has up to max_images summary values containing images. The images are built from tensor which must be 4-D with shape [batch_size, height, width, channels] and where channels can be:  1: tensor is interpreted as Grayscale. 3: tensor is interpreted as RGB. 4: tensor is interpreted as RGBA.  The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range [0, 255]. uint8 values are unchanged. The op uses two different normalization algorithms:  If the input values are all positive, they are rescaled so the largest one is 255. If any input value is negative, the values are shifted so input value 0.0 is at 127. They are then rescaled so that either the smallest value is 0, or the largest one is 255.  The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values:  If max_images is 1, the summary value tag is 'tag/image'. If max_images is greater than 1, the summary value tags are generated sequentially as 'tag/image/0', 'tag/image/1', etc.  The bad_color argument is the color to use in the generated images for non-finite input values. It is a uint8 1-D tensor of length channels. Each element must be in the range [0, 255] (It represents the value of a pixel in the output image). Non-finite values in the input tensor are replaced by this tensor in the output image. The default value is the color red.
 


 Args
  tag   A Tensor of type string. Scalar. Used to build the tag attribute of the summary values.  
  tensor   A Tensor. Must be one of the following types: uint8, float32, half, float64. 4-D of shape [batch_size, height, width, channels] where channels is 1, 3, or 4.  
  max_images   An optional int that is >= 1. Defaults to 3. Max number of batch elements to generate images for.  
  bad_color   An optional tf.TensorProto. Defaults to dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255. Color to use for pixels with non-finite values.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.ImmutableConst Returns immutable tensor from memory region.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ImmutableConst  
tf.raw_ops.ImmutableConst(
    dtype, shape, memory_region_name, name=None
)
 The current implementation memmaps the tensor from a file.
 


 Args
  dtype   A tf.DType. Type of the returned tensor.  
  shape   A tf.TensorShape or list of ints. Shape of the returned tensor.  
  memory_region_name   A string. Name of readonly memory region used by the tensor, see NewReadOnlyMemoryRegionFromFile in tensorflow::Env.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.ImportEvent  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ImportEvent  
tf.raw_ops.ImportEvent(
    writer, event, name=None
)

 


 Args
  writer   A Tensor of type resource.  
  event   A Tensor of type string.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InfeedDequeue A placeholder op for a value that will be fed into the computation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedDequeue  
tf.raw_ops.InfeedDequeue(
    dtype, shape, name=None
)

 


 Args
  dtype   A tf.DType. The type of elements in the tensor.  
  shape   A tf.TensorShape or list of ints. The shape of the tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.InfeedDequeueTuple Fetches multiple values from infeed as an XLA tuple.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedDequeueTuple  
tf.raw_ops.InfeedDequeueTuple(
    dtypes, shapes, name=None
)

 


 Args
  dtypes   A list of tf.DTypes that has length >= 1. The element types of each element in outputs.  
  shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type dtypes.
tf.raw_ops.InfeedEnqueue An op which feeds a single Tensor value into the computation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueue  
tf.raw_ops.InfeedEnqueue(
    input, shape=[], layout=[], device_ordinal=-1, name=None
)

 


 Args
  input   A Tensor. A tensor that will be provided using the infeed mechanism.  
  shape   An optional tf.TensorShape or list of ints. Defaults to []. The shape of the tensor.  
  layout   An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence. If a layout attribute is passed, but its values are all -1, the layout will be computed by the infeed operation.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InfeedEnqueuePrelinearizedBuffer An op which enqueues prelinearized buffer into TPU infeed.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueuePrelinearizedBuffer  
tf.raw_ops.InfeedEnqueuePrelinearizedBuffer(
    input, device_ordinal=-1, name=None
)

 


 Args
  input   A Tensor of type variant. A variant tensor representing linearized output.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device and = 0 when the Op is running on the CPU device.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InfeedEnqueueTuple Feeds multiple Tensor values into the computation as an XLA tuple.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueueTuple  
tf.raw_ops.InfeedEnqueueTuple(
    inputs, shapes, layouts=[], device_ordinal=-1, name=None
)

 


 Args
  inputs   A list of Tensor objects. A list of tensors that will be provided using the infeed mechanism.  
  shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in inputs.  
  layouts   An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence for all the tuple shapes, in the order the shapes appear in the "shapes" input. The layout elements for a sub-shape can be set to -1, in which case the corresponding layout will be computed by the infeed operation.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InitializeTable Table initializer that takes two tensors for keys and values respectively.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTable  
tf.raw_ops.InitializeTable(
    table_handle, keys, values, name=None
)

 


 Args
  table_handle   A Tensor of type mutable string. Handle to a table which will be initialized.  
  keys   A Tensor. Keys of type Tkey.  
  values   A Tensor. Values of type Tval.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InitializeTableFromDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromDataset  
tf.raw_ops.InitializeTableFromDataset(
    table_handle, dataset, name=None
)

 


 Args
  table_handle   A Tensor of type resource.  
  dataset   A Tensor of type variant.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InitializeTableFromTextFile Initializes a table from a text file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromTextFile  
tf.raw_ops.InitializeTableFromTextFile(
    table_handle, filename, key_index, value_index, vocab_size=-1,
    delimiter='\t', name=None
)
 It inserts one key-value pair into the table for each line of the file. The key and value is extracted from the whole line content, elements from the split line based on delimiter or the line number (starting from zero). Where to extract the key and value from a line is specified by key_index and value_index.  A value of -1 means use the line number(starting from zero), expects int64. A value of -2 means use the whole line content, expects string. A value >= 0 means use the index (starting at zero) of the split line based on delimiter. 
 


 Args
  table_handle   A Tensor of type mutable string. Handle to a table which will be initialized.  
  filename   A Tensor of type string. Filename of a vocabulary text file.  
  key_index   An int that is >= -2. Column index in a line to get the table key values from.  
  value_index   An int that is >= -2. Column index that represents information of a line to get the table value values from.  
  vocab_size   An optional int that is >= -1. Defaults to -1. Number of elements of the file, use -1 if unknown.  
  delimiter   An optional string. Defaults to "\t". Delimiter to separate fields in a line.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InitializeTableFromTextFileV2 Initializes a table from a text file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromTextFileV2  
tf.raw_ops.InitializeTableFromTextFileV2(
    table_handle, filename, key_index, value_index, vocab_size=-1,
    delimiter='\t', name=None
)
 It inserts one key-value pair into the table for each line of the file. The key and value is extracted from the whole line content, elements from the split line based on delimiter or the line number (starting from zero). Where to extract the key and value from a line is specified by key_index and value_index.  A value of -1 means use the line number(starting from zero), expects int64. A value of -2 means use the whole line content, expects string. A value >= 0 means use the index (starting at zero) of the split line based on delimiter. 
 


 Args
  table_handle   A Tensor of type resource. Handle to a table which will be initialized.  
  filename   A Tensor of type string. Filename of a vocabulary text file.  
  key_index   An int that is >= -2. Column index in a line to get the table key values from.  
  value_index   An int that is >= -2. Column index that represents information of a line to get the table value values from.  
  vocab_size   An optional int that is >= -1. Defaults to -1. Number of elements of the file, use -1 if unknown.  
  delimiter   An optional string. Defaults to "\t". Delimiter to separate fields in a line.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InitializeTableV2 Table initializer that takes two tensors for keys and values respectively.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableV2  
tf.raw_ops.InitializeTableV2(
    table_handle, keys, values, name=None
)

 


 Args
  table_handle   A Tensor of type resource. Handle to a table which will be initialized.  
  keys   A Tensor. Keys of type Tkey.  
  values   A Tensor. Values of type Tval.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.InplaceAdd Adds v into specified rows of x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InplaceAdd  
tf.raw_ops.InplaceAdd(
    x, i, v, name=None
)
 Computes y = x; y[i, :] += v; return y.
 


 Args
  x   A Tensor. A Tensor of type T.  
  i   A Tensor of type int32. A vector. Indices into the left-most dimension of x.  
  v   A Tensor. Must have the same type as x. A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.InplaceSub Subtracts v into specified rows of x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.InplaceSub  
tf.raw_ops.InplaceSub(
    x, i, v, name=None
)
 Computes y = x; y[i, :] -= v; return y.
 


 Args
  x   A Tensor. A Tensor of type T.  
  i   A Tensor of type int32. A vector. Indices into the left-most dimension of x.  
  v   A Tensor. Must have the same type as x. A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.