neulab/docprompting-conala huggingface dataset:
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient  
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(
    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, shape one of: [d], [b, d], [b, h, w, d].  
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape same as gradients. min, max: Quantization interval, floats of shape [d].  
  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 16, 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.FakeQueue Deprecated. Do not use.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQueue  
tf.raw_ops.FakeQueue(
    resource, name=None
)

 


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


 Returns   A Tensor of type mutable string.
tf.raw_ops.FFT Fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FFT  
tf.raw_ops.FFT(
    input, name=None
)
 Computes the 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.FFT2D 2D fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FFT2D  
tf.raw_ops.FFT2D(
    input, name=None
)
 Computes the 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.FFT3D 3D fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FFT3D  
tf.raw_ops.FFT3D(
    input, name=None
)
 Computes the 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.FIFOQueue A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueue  
tf.raw_ops.FIFOQueue(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)

 


 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.  
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.  
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.  
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.FIFOQueueV2 A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueueV2  
tf.raw_ops.FIFOQueueV2(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)

 


 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.  
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.  
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.  
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.Fill Creates a tensor filled with a scalar value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Fill  
tf.raw_ops.Fill(
    dims, value, name=None
)
 This operation creates a tensor of shape dims and fills it with value. For example: # Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
                     [9, 9, 9]]
 tf.fill differs from tf.constant in a few ways:  
tf.fill only supports scalar contents, whereas tf.constant supports Tensor values. 
tf.fill creates an Op in the computation graph that constructs the actual Tensor value at runtime. This is in contrast to tf.constant which embeds the entire Tensor into the graph with a Const node. Because tf.fill evaluates at graph runtime, it supports dynamic shapes based on other runtime Tensors, unlike tf.constant. 
 


 Args
  dims   A Tensor. Must be one of the following types: int32, int64. 1-D. Represents the shape of the output tensor.  
  value   A Tensor. 0-D (scalar). Value to fill the returned tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as value.  
 Numpy Compatibility Equivalent to np.full
tf.raw_ops.FilterByLastComponentDataset Creates a dataset containing elements of first component of input_dataset having true in the last component.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FilterByLastComponentDataset  
tf.raw_ops.FilterByLastComponentDataset(
    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.FilterDataset Creates a dataset containing elements of input_dataset matching predicate.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FilterDataset  
tf.raw_ops.FilterDataset(
    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.Fingerprint Generates fingerprint values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Fingerprint  
tf.raw_ops.Fingerprint(
    data, method, name=None
)
 Generates fingerprint values of data. Fingerprint op considers the first dimension of data as the batch dimension, and output[i] contains the fingerprint value generated from contents in data[i, ...] for all i. Fingerprint op writes fingerprint values as byte arrays. For example, the default method farmhash64 generates a 64-bit fingerprint value at a time. This 8-byte value is written out as an uint8 array of size 8, in little-endian order. For example, suppose that data has data type DT_INT32 and shape (2, 3, 4), and that the fingerprint method is farmhash64. In this case, the output shape is (2, 8), where 2 is the batch dimension size of data, and 8 is the size of each fingerprint value in bytes. output[0, :] is generated from 12 integers in data[0, :, :] and similarly output[1, :] is generated from other 12 integers in data[1, :, :]. Note that this op fingerprints the raw underlying buffer, and it does not fingerprint Tensor's metadata such as data type and/or shape. For example, the fingerprint values are invariant under reshapes and bitcasts as long as the batch dimension remain the same: Fingerprint(data) == Fingerprint(Reshape(data, ...))
Fingerprint(data) == Fingerprint(Bitcast(data, ...))
 For string data, one should expect Fingerprint(data) != Fingerprint(ReduceJoin(data)) in general.
 


 Args
  data   A Tensor. Must have rank 1 or higher.  
  method   A Tensor of type string. Fingerprint method used by this op. Currently available method is farmhash::fingerprint64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type uint8.
tf.raw_ops.FixedLengthRecordDataset Creates a dataset that emits the records from one or more binary files.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDataset  
tf.raw_ops.FixedLengthRecordDataset(
    filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name=None
)

 


 Args
  filenames   A Tensor of type string. A scalar or a vector containing the name(s) of the file(s) to be read.  
  header_bytes   A Tensor of type int64. A scalar representing the number of bytes to skip at the beginning of a file.  
  record_bytes   A Tensor of type int64. A scalar representing the number of bytes in each record.  
  footer_bytes   A Tensor of type int64. A scalar representing the number of bytes to skip at the end of a file.  
  buffer_size   A Tensor of type int64. A scalar representing the number of bytes to buffer. Must be > 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.FixedLengthRecordDatasetV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDatasetV2  
tf.raw_ops.FixedLengthRecordDatasetV2(
    filenames, header_bytes, record_bytes, footer_bytes, buffer_size,
    compression_type, name=None
)

 


 Args
  filenames   A Tensor of type string.  
  header_bytes   A Tensor of type int64.  
  record_bytes   A Tensor of type int64.  
  footer_bytes   A Tensor of type int64.  
  buffer_size   A Tensor of type int64.  
  compression_type   A Tensor of type string.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.FixedLengthRecordReader A Reader that outputs fixed-length records from a file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReader  
tf.raw_ops.FixedLengthRecordReader(
    record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
    container='', shared_name='', name=None
)

 


 Args
  record_bytes   An int. Number of bytes in the record.  
  header_bytes   An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.  
  footer_bytes   An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.  
  hop_bytes   An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.  
  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 mutable string.
tf.raw_ops.FixedLengthRecordReaderV2 A Reader that outputs fixed-length records from a file.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReaderV2  
tf.raw_ops.FixedLengthRecordReaderV2(
    record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
    container='', shared_name='', encoding='',
    name=None
)

 


 Args
  record_bytes   An int. Number of bytes in the record.  
  header_bytes   An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.  
  footer_bytes   An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.  
  hop_bytes   An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.  
  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.  
  encoding   An optional string. Defaults to "". The type of encoding for the file. Currently ZLIB and GZIP are supported. Defaults to none.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.FlatMapDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FlatMapDataset  
tf.raw_ops.FlatMapDataset(
    input_dataset, other_arguments, f, output_types, output_shapes, name=None
)
 Unlike MapDataset, the f in FlatMapDataset is expected to return a Dataset variant, and FlatMapDataset will flatten successive results into a single Dataset.
 


 Args
  input_dataset   A Tensor of type variant.  
  other_arguments   A list of Tensor objects.  
  f   A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.  
  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.Floor Returns element-wise largest integer not greater than x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Floor  
tf.raw_ops.Floor(
    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.FloorDiv Returns x // y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FloorDiv  
tf.raw_ops.FloorDiv(
    x, y, name=None
)
 
Note: floor_div supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.FloorMod Returns element-wise remainder of division. When x < 0 xor y < 0 is  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FloorMod  
tf.raw_ops.FloorMod(
    x, y, name=None
)
 true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x. 
Note: math.floormod supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: int32, int64, uint64, bfloat16, half, float32, float64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.FlushSummaryWriter  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FlushSummaryWriter  
tf.raw_ops.FlushSummaryWriter(
    writer, name=None
)

 


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


 Returns   The created Operation.
tf.raw_ops.For 
<section class="expandable">
  <h4 class="showalways">View aliases</h4>
  <p>
<b>Compat aliases for migration</b>
<p>See
<a href="https://www.tensorflow.org/guide/migrate">Migration guide</a> for
more details.</p>
<p>`tf.compat.v1.raw_ops.For`</p>
</p>
</section>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>tf.raw_ops.For(
    start, limit, delta, input, body, name=None
)
</code></pre>



<!-- Placeholder for "Used in" -->
 output = input;
 for i in range(start, limit, delta)
   output = body(i, output);

 


 Args
  start   A Tensor of type int32. The lower bound. An int32  
  limit   A Tensor of type int32. The upper bound. An int32  
  delta   A Tensor of type int32. The increment. An int32  
  input   A list of Tensor objects. A list of input tensors whose types are T.  
  body   A function decorated with @Defun. A function that takes a list of tensors (int32, T) and returns another list of tensors (T).  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects. Has the same type as input.
tf.raw_ops.FractionalAvgPool Performs fractional average pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPool  
tf.raw_ops.FractionalAvgPool(
    value, pooling_ratio, pseudo_random=False, overlapping=False,
    deterministic=False, seed=0, seed2=0, name=None
)
 Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region.
 


 Args
  value   A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].  
  pooling_ratio   A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.  
  pseudo_random   An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.  
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. 
 
  deterministic   An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic.  
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.  
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence).     output   A Tensor. Has the same type as value.  
  row_pooling_sequence   A Tensor of type int64.  
  col_pooling_sequence   A Tensor of type int64.
tf.raw_ops.FractionalAvgPoolGrad Computes gradient of the FractionalAvgPool function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPoolGrad  
tf.raw_ops.FractionalAvgPoolGrad(
    orig_input_tensor_shape, out_backprop, row_pooling_sequence,
    col_pooling_sequence, overlapping=False, name=None
)
 Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.
 


 Args
  orig_input_tensor_shape   A Tensor of type int64. Original input tensor shape for fractional_avg_pool  
  out_backprop   A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_avg_pool.  
  row_pooling_sequence   A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.  
  col_pooling_sequence   A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.  
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. 
 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as out_backprop.