neulab/docprompting-conala huggingface dataset:
tf.raw_ops.Dilation2D Computes the grayscale dilation of 4-D input and 3-D filter tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Dilation2D  
tf.raw_ops.Dilation2D(
    input, filter, strides, rates, padding, name=None
)
 The input tensor has shape [batch, in_height, in_width, depth] and the filter tensor has shape [filter_height, filter_width, depth], i.e., each input channel is processed independently of the others with its own structuring function. The output tensor has shape [batch, out_height, out_width, depth]. The spatial dimensions of the output tensor depend on the padding algorithm. We currently only support the default "NHWC" data_format. In detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with conv2d, we use unmirrored filters): output[b, y, x, c] =
   max_{dy, dx} input[b,
                      strides[1] * y + rates[1] * dy,
                      strides[2] * x + rates[2] * dx,
                      c] +
                filter[dy, dx, c]
 Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros. Note on duality: The dilation of input by the filter is equal to the negation of the erosion of -input by the reflected filter.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D with shape [batch, in_height, in_width, depth].  
  filter   A Tensor. Must have the same type as input. 3-D with shape [filter_height, filter_width, depth].  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor. Must be: [1, stride_height, stride_width, 1].  
  rates   A list of ints that has length >= 4. The input stride for atrous morphological dilation. Must be: [1, rate_height, rate_width, 1].  
  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 input.
tf.raw_ops.Dilation2DBackpropFilter Computes the gradient of morphological 2-D dilation with respect to the filter.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Dilation2DBackpropFilter  
tf.raw_ops.Dilation2DBackpropFilter(
    input, filter, out_backprop, strides, rates, 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. 4-D with shape [batch, in_height, in_width, depth].  
  filter   A Tensor. Must have the same type as input. 3-D with shape [filter_height, filter_width, depth].  
  out_backprop   A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, depth].  
  strides   A list of ints that has length >= 4. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: [1, stride_height, stride_width, 1].  
  rates   A list of ints that has length >= 4. 1-D of length 4. The input stride for atrous morphological dilation. Must be: [1, rate_height, rate_width, 1].  
  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 input.
tf.raw_ops.Dilation2DBackpropInput Computes the gradient of morphological 2-D dilation with respect to the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Dilation2DBackpropInput  
tf.raw_ops.Dilation2DBackpropInput(
    input, filter, out_backprop, strides, rates, 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. 4-D with shape [batch, in_height, in_width, depth].  
  filter   A Tensor. Must have the same type as input. 3-D with shape [filter_height, filter_width, depth].  
  out_backprop   A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, depth].  
  strides   A list of ints that has length >= 4. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: [1, stride_height, stride_width, 1].  
  rates   A list of ints that has length >= 4. 1-D of length 4. The input stride for atrous morphological dilation. Must be: [1, rate_height, rate_width, 1].  
  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 input.
tf.raw_ops.DirectedInterleaveDataset A substitute for InterleaveDataset on a fixed list of N datasets.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DirectedInterleaveDataset  
tf.raw_ops.DirectedInterleaveDataset(
    selector_input_dataset, data_input_datasets, output_types, output_shapes,
    name=None
)

 


 Args
  selector_input_dataset   A Tensor of type variant. A dataset of scalar DT_INT64 elements that determines which of the N data inputs should produce the next output element.  
  data_input_datasets   A list of at least 1 Tensor objects with type variant. N datasets with the same type that will be interleaved according to the values of selector_input_dataset.  
  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.Div Returns x / y element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Div  
tf.raw_ops.Div(
    x, y, name=None
)
 
Note: 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.DivNoNan Returns 0 if the denominator is zero.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DivNoNan  
tf.raw_ops.DivNoNan(
    x, y, name=None
)
 
Note: DivNoNan supports broadcasting. More about broadcasting here

 


 Args
  x   A Tensor. Must be one of the following types: half, float32, float64, 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.DrawBoundingBoxes Draw bounding boxes on a batch of images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DrawBoundingBoxes  
tf.raw_ops.DrawBoundingBoxes(
    images, boxes, name=None
)
 Outputs a copy of images but draws on top of the pixels zero or more bounding boxes specified by the locations in boxes. The coordinates of the each bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image. For example, if an image is 100 x 200 pixels (height x width) and the bounding box is [0.1, 0.2, 0.5, 0.9], the upper-left and bottom-right coordinates of the bounding box will be (40, 10) to (180, 50) (in (x,y) coordinates). Parts of the bounding box may fall outside the image.
 


 Args
  images   A Tensor. Must be one of the following types: float32, half. 4-D with shape [batch, height, width, depth]. A batch of images.  
  boxes   A Tensor of type float32. 3-D with shape [batch, num_bounding_boxes, 4] containing bounding boxes.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.DrawBoundingBoxesV2 Draw bounding boxes on a batch of images.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DrawBoundingBoxesV2  
tf.raw_ops.DrawBoundingBoxesV2(
    images, boxes, colors, name=None
)
 Outputs a copy of images but draws on top of the pixels zero or more bounding boxes specified by the locations in boxes. The coordinates of the each bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image. For example, if an image is 100 x 200 pixels (height x width) and the bounding box is [0.1, 0.2, 0.5, 0.9], the upper-left and bottom-right coordinates of the bounding box will be (40, 10) to (100, 50) (in (x,y) coordinates). Parts of the bounding box may fall outside the image.
 


 Args
  images   A Tensor. Must be one of the following types: float32, half. 4-D with shape [batch, height, width, depth]. A batch of images.  
  boxes   A Tensor of type float32. 3-D with shape [batch, num_bounding_boxes, 4] containing bounding boxes.  
  colors   A Tensor of type float32. 2-D. A list of RGBA colors to cycle through for the boxes.  
  name   A name for the operation (optional).   
 


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

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.DummyMemoryCache  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DummyMemoryCache  
tf.raw_ops.DummyMemoryCache(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.DummySeedGenerator  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DummySeedGenerator  
tf.raw_ops.DummySeedGenerator(
    name=None
)

 


 Args
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.DynamicPartition Partitions data into num_partitions tensors using indices from partitions.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DynamicPartition  
tf.raw_ops.DynamicPartition(
    data, partitions, num_partitions, name=None
)
 For each index tuple js of size partitions.ndim, the slice data[js, ...] becomes part of outputs[partitions[js]]. The slices with partitions[js] = i are placed in outputs[i] in lexicographic order of js, and the first dimension of outputs[i] is the number of entries in partitions equal to i. In detail, outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]

outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
 data.shape must start with partitions.shape. For example: # Scalar partitions.
partitions = 1
num_partitions = 2
data = [10, 20]
outputs[0] = []  # Empty with shape [0, 2]
outputs[1] = [[10, 20]]

# Vector partitions.
partitions = [0, 0, 1, 1, 0]
num_partitions = 2
data = [10, 20, 30, 40, 50]
outputs[0] = [10, 20, 50]
outputs[1] = [30, 40]
 See dynamic_stitch for an example on how to merge partitions back.   
 


 Args
  data   A Tensor.  
  partitions   A Tensor of type int32. Any shape. Indices in the range [0, num_partitions).  
  num_partitions   An int that is >= 1. The number of partitions to output.  
  name   A name for the operation (optional).   
 


 Returns   A list of num_partitions Tensor objects with the same type as data.
tf.raw_ops.DynamicStitch Interleave the values from the data tensors into a single tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DynamicStitch  
tf.raw_ops.DynamicStitch(
    indices, data, name=None
)
 Builds a merged tensor such that merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
 For example, if each indices[m] is scalar or vector, we have # Scalar indices:
merged[indices[m], ...] = data[m][...]

# Vector indices:
merged[indices[m][i], ...] = data[m][i, ...]
 Each data[i].shape must start with the corresponding indices[i].shape, and the rest of data[i].shape must be constant w.r.t. i. That is, we must have data[i].shape = indices[i].shape + constant. In terms of this constant, the output shape is merged.shape = [max(indices)] + constant
 Values are merged in order, so if an index appears in both indices[m][i] and indices[n][j] for (m,i) < (n,j) the slice data[n][j] will appear in the merged result. If you do not need this guarantee, ParallelDynamicStitch might perform better on some devices. For example: indices[0] = 6
indices[1] = [4, 1]
indices[2] = [[5, 2], [0, 3]]
data[0] = [61, 62]
data[1] = [[41, 42], [11, 12]]
data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
          [51, 52], [61, 62]]
 This method can be used to merge partitions created by dynamic_partition as illustrated on the following example: # Apply function (increments x_i) on elements for which a certain condition
# apply (x_i != -1 in this example).
x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
condition_mask=tf.not_equal(x,tf.constant(-1.))
partitioned_data = tf.dynamic_partition(
    x, tf.cast(condition_mask, tf.int32) , 2)
partitioned_data[1] = partitioned_data[1] + 1.0
condition_indices = tf.dynamic_partition(
    tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
x = tf.dynamic_stitch(condition_indices, partitioned_data)
# Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
# unchanged.
   
 


 Args
  indices   A list of at least 1 Tensor objects with type int32.  
  data   A list with the same length as indices of Tensor objects with the same type.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.EagerPyFunc Eagerly executes a python function to compute func(input)->output. The  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EagerPyFunc  
tf.raw_ops.EagerPyFunc(
    input, token, Tout, is_async=False, name=None
)
 semantics of the input, output, and attributes are the same as those for PyFunc.
 


 Args
  input   A list of Tensor objects.  
  token   A string.  
  Tout   A list of tf.DTypes.  
  is_async   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.EditDistance Computes the (possibly normalized) Levenshtein Edit Distance.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EditDistance  
tf.raw_ops.EditDistance(
    hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices,
    truth_values, truth_shape, normalize=True, name=None
)
 The inputs are variable-length sequences provided by SparseTensors (hypothesis_indices, hypothesis_values, hypothesis_shape) and (truth_indices, truth_values, truth_shape). The inputs are:
 


 Args
  hypothesis_indices   A Tensor of type int64. The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix.  
  hypothesis_values   A Tensor. The values of the hypothesis list SparseTensor. This is an N-length vector.  
  hypothesis_shape   A Tensor of type int64. The shape of the hypothesis list SparseTensor. This is an R-length vector.  
  truth_indices   A Tensor of type int64. The indices of the truth list SparseTensor. This is an M x R int64 matrix.  
  truth_values   A Tensor. Must have the same type as hypothesis_values. The values of the truth list SparseTensor. This is an M-length vector.  
  truth_shape   A Tensor of type int64. truth indices, vector.  
  normalize   An optional bool. Defaults to True. boolean (if true, edit distances are normalized by length of truth). The output is: 
 
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.Eig Computes the eigen decomposition of one or more square matrices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Eig  
tf.raw_ops.Eig(
    input, Tout, compute_v=True, name=None
)
 Computes the eigenvalues and (optionally) right eigenvectors of each inner matrix in input such that input[..., :, :] = v[..., :, :] * diag(e[..., :]). The eigenvalues are sorted in non-decreasing order. # a is a tensor.
# e is a tensor of eigenvalues.
# v is a tensor of eigenvectors.
e, v = eig(a)
e = eig(a, compute_v=False)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, complex64, complex128. Tensor input of shape [N, N].  
  Tout   A tf.DType from: tf.complex64, tf.complex128.  
  compute_v   An optional bool. Defaults to True. If True then eigenvectors will be computed and returned in v. Otherwise, only the eigenvalues will be computed.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (e, v).     e   A Tensor of type Tout.  
  v   A Tensor of type Tout.
tf.raw_ops.Elu Computes exponential linear: exp(features) - 1 if < 0, features otherwise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Elu  
tf.raw_ops.Elu(
    features, name=None
)
 See Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) 
 


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


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.EluGrad Computes gradients for the exponential linear (Elu) operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EluGrad  
tf.raw_ops.EluGrad(
    gradients, outputs, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding Elu operation.  
  outputs   A Tensor. Must have the same type as gradients. The outputs of the corresponding Elu operation.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.Empty Creates a tensor with the given shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Empty  
tf.raw_ops.Empty(
    shape, dtype, init=False, name=None
)
 This operation creates a tensor of shape and dtype. Args: shape: A Tensor of type int32. 1-D. Represents the shape of the output tensor. dtype: A tf.DType. init: An optional bool. Defaults to False. If True, initialize the returned tensor with the default value of dtype. Otherwise, the implementation is free not to initializethe tensor's content. name: A name for the operation (optional). Returns: A Tensor of type dtype.
tf.raw_ops.EmptyTensorList Creates and returns an empty tensor list.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EmptyTensorList  
tf.raw_ops.EmptyTensorList(
    element_shape, max_num_elements, element_dtype, name=None
)
 All list elements must be tensors of dtype element_dtype and shape compatible with element_shape. handle: an empty tensor list. element_dtype: the type of elements in the list. element_shape: a shape compatible with that of elements in the list.
 


 Args
  element_shape   A Tensor. Must be one of the following types: int32, int64.  
  max_num_elements   A Tensor of type int32.  
  element_dtype   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.EncodeBase64 Encode strings into web-safe base64 format.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodeBase64  
tf.raw_ops.EncodeBase64(
    input, pad=False, name=None
)
 Refer to the following article for more information on base64 format: en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the end so that the encoded has length multiple of 4. See Padding section of the link above. Web-safe means that the encoder uses - and _ instead of + and /.
 


 Args
  input   A Tensor of type string. Strings to be encoded.  
  pad   An optional bool. Defaults to False. Bool whether padding is applied at the ends.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.EncodeJpeg JPEG-encode an image.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodeJpeg  
tf.raw_ops.EncodeJpeg(
    image, format='', quality=95, progressive=False, optimize_size=False,
    chroma_downsampling=True, density_unit='in', x_density=300,
    y_density=300, xmp_metadata='', name=None
)
 image is a 3-D uint8 Tensor of shape [height, width, channels]. The attr format can be used to override the color format of the encoded output. Values can be:  
'': Use a default format based on the number of channels in the image. 
grayscale: Output a grayscale JPEG image. The channels dimension of image must be 1. 
rgb: Output an RGB JPEG image. The channels dimension of image must be 3.  If format is not specified or is the empty string, a default format is picked in function of the number of channels in image:  1: Output a grayscale image. 3: Output an RGB image. 
 


 Args
  image   A Tensor of type uint8. 3-D with shape [height, width, channels].  
  format   An optional string from: "", "grayscale", "rgb". Defaults to "". Per pixel image format.  
  quality   An optional int. Defaults to 95. Quality of the compression from 0 to 100 (higher is better and slower).  
  progressive   An optional bool. Defaults to False. If True, create a JPEG that loads progressively (coarse to fine).  
  optimize_size   An optional bool. Defaults to False. If True, spend CPU/RAM to reduce size with no quality change.  
  chroma_downsampling   An optional bool. Defaults to True. See http://en.wikipedia.org/wiki/Chroma_subsampling  
  density_unit   An optional string from: "in", "cm". Defaults to "in". Unit used to specify x_density and y_density: pixels per inch ('in') or centimeter ('cm').  
  x_density   An optional int. Defaults to 300. Horizontal pixels per density unit.  
  y_density   An optional int. Defaults to 300. Vertical pixels per density unit.  
  xmp_metadata   An optional string. Defaults to "". If not empty, embed this XMP metadata in the image header.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.EncodeJpegVariableQuality JPEG encode input image with provided compression quality.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodeJpegVariableQuality  
tf.raw_ops.EncodeJpegVariableQuality(
    images, quality, name=None
)
 image is a 3-D uint8 Tensor of shape [height, width, channels]. quality is an int32 jpeg compression quality value between 0 and 100.
 


 Args
  images   A Tensor of type uint8. Images to adjust. At least 3-D.  
  quality   A Tensor of type int32. An int quality to encode to.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.