neulab/docprompting-conala huggingface dataset:
tf.raw_ops.GatherV2 Gather slices from params axis axis according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GatherV2  
tf.raw_ops.GatherV2(
    params, indices, axis, batch_dims=0, name=None
)
 indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape params.shape[:axis] + indices.shape[batch_dims:] + params.shape[axis + 1:] where: # Scalar indices (output is rank(params) - 1).
output[a_0, ..., a_n, b_0, ..., b_n] =
  params[a_0, ..., a_n, indices, b_0, ..., b_n]

# Vector indices (output is rank(params)).
output[a_0, ..., a_n, i, b_0, ..., b_n] =
  params[a_0, ..., a_n, indices[i], b_0, ..., b_n]

# Higher rank indices (output is rank(params) + rank(indices) - 1).
output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
  params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
    Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value. See also tf.batch_gather and tf.gather_nd.
 


 Args
  params   A Tensor. The tensor from which to gather values. Must be at least rank axis + 1.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor. Must be in range [0, params.shape[axis]).  
  axis   A Tensor. Must be one of the following types: int32, int64. The axis in params to gather indices from. Defaults to the first dimension. Supports negative indexes.  
  batch_dims   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as params.
tf.raw_ops.GenerateBoundingBoxProposals This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GenerateBoundingBoxProposals  
tf.raw_ops.GenerateBoundingBoxProposals(
    scores, bbox_deltas, image_info, anchors, nms_threshold, pre_nms_topn, min_size,
    post_nms_topn=300, name=None
)
 The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors,
applies non-maximal suppression on overlapping boxes with higher than
`nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter
side is less than `min_size`.
Inputs:
`scores`: A 4D tensor of shape [Batch, Height, Width, Num Anchors] containing the scores per anchor at given position
`bbox_deltas`: is a tensor of shape [Batch, Height, Width, 4 x Num Anchors] boxes encoded to each anchor
`anchors`: A 1D tensor of shape [4 x Num Anchors], representing the anchors.
Outputs:
`rois`: output RoIs, a 3D tensor of shape [Batch, post_nms_topn, 4], padded by 0 if less than post_nms_topn candidates found.
`roi_probabilities`: probability scores of each roi in 'rois', a 2D tensor of shape [Batch,post_nms_topn], padded with 0 if needed, sorted by scores.

 


 Args
  scores   A Tensor of type float32. A 4-D float tensor of shape [num_images, height, width, num_achors] containing scores of the boxes for given anchors, can be unsorted.  
  bbox_deltas   A Tensor of type float32. A 4-D float tensor of shape [num_images, height, width, 4 x num_anchors]. encoding boxes with respec to each anchor. Coordinates are given in the form [dy, dx, dh, dw].  
  image_info   A Tensor of type float32. A 2-D float tensor of shape [num_images, 5] containing image information Height, Width, Scale.  
  anchors   A Tensor of type float32. A 2-D float tensor of shape [num_anchors, 4] describing the anchor boxes. Boxes are formatted in the form [y1, x1, y2, x2].  
  nms_threshold   A Tensor of type float32. A scalar float tensor for non-maximal-suppression threshold.  
  pre_nms_topn   A Tensor of type int32. A scalar int tensor for the number of top scoring boxes to be used as input.  
  min_size   A Tensor of type float32. A scalar float tensor. Any box that has a smaller size than min_size will be discarded.  
  post_nms_topn   An optional int. Defaults to 300. An integer. Maximum number of rois in the output.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (rois, roi_probabilities).     rois   A Tensor of type float32.  
  roi_probabilities   A Tensor of type float32.
tf.raw_ops.GenerateVocabRemapping Given a path to new and old vocabulary files, returns a remapping Tensor of  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GenerateVocabRemapping  
tf.raw_ops.GenerateVocabRemapping(
    new_vocab_file, old_vocab_file, new_vocab_offset, num_new_vocab,
    old_vocab_size=-1, name=None
)
 length num_new_vocab, where remapping[i] contains the row number in the old vocabulary that corresponds to row i in the new vocabulary (starting at line new_vocab_offset and up to num_new_vocab entities), or -1 if entry i in the new vocabulary is not in the old vocabulary. The old vocabulary is constrained to the first old_vocab_size entries if old_vocab_size is not the default value of -1. num_vocab_offset enables use in the partitioned variable case, and should generally be set through examining partitioning info. The format of the files should be a text file, with each line containing a single entity within the vocabulary. For example, with new_vocab_file a text file containing each of the following elements on a single line: [f0, f1, f2, f3], old_vocab_file = [f1, f0, f3], num_new_vocab = 3, new_vocab_offset = 1, the returned remapping would be [0, -1, 2]. The op also returns a count of how many entries in the new vocabulary were present in the old vocabulary, which is used to calculate the number of values to initialize in a weight matrix remapping This functionality can be used to remap both row vocabularies (typically, features) and column vocabularies (typically, classes) from TensorFlow checkpoints. Note that the partitioning logic relies on contiguous vocabularies corresponding to div-partitioned variables. Moreover, the underlying remapping uses an IndexTable (as opposed to an inexact CuckooTable), so client code should use the corresponding index_table_from_file() as the FeatureColumn framework does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
 


 Args
  new_vocab_file   A Tensor of type string. Path to the new vocab file.  
  old_vocab_file   A Tensor of type string. Path to the old vocab file.  
  new_vocab_offset   An int that is >= 0. How many entries into the new vocab file to start reading.  
  num_new_vocab   An int that is >= 0. Number of entries in the new vocab file to remap.  
  old_vocab_size   An optional int that is >= -1. Defaults to -1. Number of entries in the old vocab file to consider. If -1, use the entire old vocabulary.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (remapping, num_present).     remapping   A Tensor of type int64.  
  num_present   A Tensor of type int32.
tf.raw_ops.GeneratorDataset Creates a dataset that invokes a function to generate elements.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GeneratorDataset  
tf.raw_ops.GeneratorDataset(
    init_func_other_args, next_func_other_args, finalize_func_other_args, init_func,
    next_func, finalize_func, output_types, output_shapes, name=None
)

 


 Args
  init_func_other_args   A list of Tensor objects.  
  next_func_other_args   A list of Tensor objects.  
  finalize_func_other_args   A list of Tensor objects.  
  init_func   A function decorated with @Defun.  
  next_func   A function decorated with @Defun.  
  finalize_func   A function decorated with @Defun.  
  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.GetSessionHandle Store the input tensor in the state of the current session.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionHandle  
tf.raw_ops.GetSessionHandle(
    value, name=None
)

 


 Args
  value   A Tensor. The tensor to be stored.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.GetSessionHandleV2 Store the input tensor in the state of the current session.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionHandleV2  
tf.raw_ops.GetSessionHandleV2(
    value, name=None
)

 


 Args
  value   A Tensor. The tensor to be stored.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.GetSessionTensor Get the value of the tensor specified by its handle.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionTensor  
tf.raw_ops.GetSessionTensor(
    handle, dtype, name=None
)

 


 Args
  handle   A Tensor of type string. The handle for a tensor stored in the session state.  
  dtype   A tf.DType. The type of the output value.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.Greater Returns the truth value of (x > y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Greater  
tf.raw_ops.Greater(
    x, y, name=None
)
 
Note: math.greater supports broadcasting. More about broadcasting here
 Example: x = tf.constant([5, 4, 6])
y = tf.constant([5, 2, 5])
tf.math.greater(x, y) ==> [False, True, True]

x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.greater(x, y) ==> [False, False, True]

 


 Args
  x   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.GreaterEqual Returns the truth value of (x >= y) element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GreaterEqual  
tf.raw_ops.GreaterEqual(
    x, y, name=None
)
 
Note: math.greater_equal supports broadcasting. More about broadcasting here
 Example: x = tf.constant([5, 4, 6, 7])
y = tf.constant([5, 2, 5, 10])
tf.math.greater_equal(x, y) ==> [True, True, True, False]

x = tf.constant([5, 4, 6, 7])
y = tf.constant([5])
tf.math.greater_equal(x, y) ==> [True, False, True, True]

 


 Args
  x   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.GroupByReducerDataset Creates a dataset that computes a group-by on input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GroupByReducerDataset  
tf.raw_ops.GroupByReducerDataset(
    input_dataset, key_func_other_arguments, init_func_other_arguments,
    reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func,
    reduce_func, finalize_func, output_types, output_shapes, name=None
)
 Creates a dataset that computes a group-by on input_dataset.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  key_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for key_func.  
  init_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for init_func.  
  reduce_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for reduce_func.  
  finalize_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for finalize_func.  
  key_func   A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.  
  init_func   A function decorated with @Defun. A function mapping a key of type DT_INT64, concatenated with init_func_other_arguments to the initial reducer state.  
  reduce_func   A function decorated with @Defun. A function mapping the current reducer state and an element of input_dataset, concatenated with reduce_func_other_arguments to a new reducer state.  
  finalize_func   A function decorated with @Defun. A function mapping the final reducer state to an output element.  
  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.GroupByWindowDataset Creates a dataset that computes a windowed group-by on input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GroupByWindowDataset  
tf.raw_ops.GroupByWindowDataset(
    input_dataset, key_func_other_arguments, reduce_func_other_arguments,
    window_size_func_other_arguments, key_func, reduce_func, window_size_func,
    output_types, output_shapes, name=None
)
 //
 


 Args
  input_dataset   A Tensor of type variant.  
  key_func_other_arguments   A list of Tensor objects.  
  reduce_func_other_arguments   A list of Tensor objects.  
  window_size_func_other_arguments   A list of Tensor objects.  
  key_func   A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.  
  reduce_func   A function decorated with @Defun.  
  window_size_func   A function decorated with @Defun.  
  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.GRUBlockCell Computes the GRU cell forward propagation for 1 time step.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GRUBlockCell  
tf.raw_ops.GRUBlockCell(
    x, h_prev, w_ru, w_c, b_ru, b_c, name=None
)
 Args x: Input to the GRU cell. h_prev: State input from the previous GRU cell. w_ru: Weight matrix for the reset and update gate. w_c: Weight matrix for the cell connection gate. b_ru: Bias vector for the reset and update gate. b_c: Bias vector for the cell connection gate. Returns r: Output of the reset gate. u: Output of the update gate. c: Output of the cell connection gate. h: Current state of the GRU cell. Note on notation of the variables: Concatenation of a and b is represented by a_b Element-wise dot product of a and b is represented by ab Element-wise dot product is represented by \circ Matrix multiplication is represented by * Biases are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0) This kernel op implements the following mathematical equations: x_h_prev = [x, h_prev]

[r_bar u_bar] = x_h_prev * w_ru + b_ru

r = sigmoid(r_bar)
u = sigmoid(u_bar)

h_prevr = h_prev \circ r

x_h_prevr = [x h_prevr]

c_bar = x_h_prevr * w_c + b_c
c = tanh(c_bar)

h = (1-u) \circ c + u \circ h_prev

 


 Args
  x   A Tensor. Must be one of the following types: float32.  
  h_prev   A Tensor. Must have the same type as x.  
  w_ru   A Tensor. Must have the same type as x.  
  w_c   A Tensor. Must have the same type as x.  
  b_ru   A Tensor. Must have the same type as x.  
  b_c   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (r, u, c, h).     r   A Tensor. Has the same type as x.  
  u   A Tensor. Has the same type as x.  
  c   A Tensor. Has the same type as x.  
  h   A Tensor. Has the same type as x.
tf.raw_ops.GuaranteeConst Gives a guarantee to the TF runtime that the input tensor is a constant.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GuaranteeConst  
tf.raw_ops.GuaranteeConst(
    input, name=None
)
 The runtime is then free to make optimizations based on this. Only accepts value typed tensors as inputs and rejects resource variable handles as input. Returns the input tensor without modification.
 


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


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.HashTable Creates a non-initialized hash table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.HashTable  
tf.raw_ops.HashTable(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, name=None
)
 This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.
 


 Args
  key_dtype   A tf.DType. Type of the table keys.  
  value_dtype   A tf.DType. Type of the table values.  
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.  
  use_node_name_sharing   An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type mutable string.
tf.raw_ops.HashTableV2 Creates a non-initialized hash table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.HashTableV2  
tf.raw_ops.HashTableV2(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, name=None
)
 This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.
 


 Args
  key_dtype   A tf.DType. Type of the table keys.  
  value_dtype   A tf.DType. Type of the table values.  
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.  
  use_node_name_sharing   An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.HistogramFixedWidth Return histogram of values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.HistogramFixedWidth  
tf.raw_ops.HistogramFixedWidth(
    values, value_range, nbins, dtype=tf.dtypes.int32, name=None
)
 Given the tensor values, this operation returns a rank 1 histogram counting the number of entries in values that fall into every bin. The bins are equal width and determined by the arguments value_range and nbins. # Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]

with tf.get_default_session() as sess:
  hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
  variables.global_variables_initializer().run()
  sess.run(hist) => [2, 1, 1, 0, 2]

 


 Args
  values   A Tensor. Must be one of the following types: int32, int64, float32, float64. Numeric Tensor.  
  value_range   A Tensor. Must have the same type as values. Shape [2] Tensor of same dtype as values. values <= value_range[0] will be mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].  
  nbins   A Tensor of type int32. Scalar int32 Tensor. Number of histogram bins.  
  dtype   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.HistogramSummary Outputs a Summary protocol buffer with a histogram.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.HistogramSummary  
tf.raw_ops.HistogramSummary(
    tag, values, name=None
)
 The generated Summary has one summary value containing a histogram for values. This op reports an InvalidArgument error if any value is not finite.
 


 Args
  tag   A Tensor of type string. Scalar. Tag to use for the Summary.Value.  
  values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. Any shape. Values to use to build the histogram.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.HSVToRGB Convert one or more images from HSV to RGB.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.HSVToRGB  
tf.raw_ops.HSVToRGB(
    images, name=None
)
 Outputs a tensor of the same shape as the images tensor, containing the RGB value of the pixels. The output is only well defined if the value in images are in [0,1]. See rgb_to_hsv for a description of the HSV encoding.
 


 Args
  images   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 1-D or higher rank. HSV data to convert. Last dimension must be size 3.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.Identity Return a tensor with the same shape and contents as the input tensor or value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Identity  
tf.raw_ops.Identity(
    input, name=None
)

 


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


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.IdentityN Returns a list of tensors with the same shapes and contents as the input  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.IdentityN  
tf.raw_ops.IdentityN(
    input, name=None
)
 tensors. This op can be used to override the gradient for complicated functions. For example, suppose y = f(x) and we wish to apply a custom function g for backprop such that dx = g(dy). In Python, with tf.get_default_graph().gradient_override_map(
    {'IdentityN': 'OverrideGradientWithG'}):
  y, _ = identity_n([f(x), x])

@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
  return [None, g(dy)]  # Do not backprop to f(x).

 


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


 Returns   A list of Tensor objects. Has the same type as input.
tf.raw_ops.IdentityReader 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.IdentityReader  
tf.raw_ops.IdentityReader(
    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 mutable string.