neulab/docprompting-conala huggingface dataset:
tf.raw_ops.DenseBincount Counts the number of occurrences of each value in an integer array.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseBincount  
tf.raw_ops.DenseBincount(
    input, size, weights, binary_output=False, name=None
)
 Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i. Values in arr outside of the range [0, size) are ignored.
 


 Args
  input   A Tensor. Must be one of the following types: int32, int64. 1D or 2D int Tensor.  
  size   A Tensor. Must have the same type as input. non-negative int scalar Tensor.  
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. is an int32, int64, float32, or float64 Tensor with the same shape as arr, or a length-0 Tensor, in which case it acts as all weights equal to 1.  
  binary_output   An optional bool. Defaults to False. bool; Whether the kernel should count the appearance or number of occurrences.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as weights.
tf.raw_ops.DenseCountSparseOutput Performs sparse-output bin counting for a tf.tensor input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseCountSparseOutput  
tf.raw_ops.DenseCountSparseOutput(
    values, weights, binary_output, minlength=-1, maxlength=-1, name=None
)
 Counts the number of times each value occurs in the input.
 


 Args
  values   A Tensor. Must be one of the following types: int32, int64. Tensor containing data to count.  
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. A Tensor of the same shape as indices containing per-index weight values. May also be the empty tensor if no weights are used.  
  binary_output   A bool. Whether to output the number of occurrences of each value or 1.  
  minlength   An optional int that is >= -1. Defaults to -1. Minimum value to count. Can be set to -1 for no minimum.  
  maxlength   An optional int that is >= -1. Defaults to -1. Maximum value to count. Can be set to -1 for no maximum.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_indices, output_values, output_dense_shape).     output_indices   A Tensor of type int64.  
  output_values   A Tensor. Has the same type as weights.  
  output_dense_shape   A Tensor of type int64.
tf.raw_ops.DenseToCSRSparseMatrix Converts a dense tensor to a (possibly batched) CSRSparseMatrix.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseToCSRSparseMatrix  
tf.raw_ops.DenseToCSRSparseMatrix(
    dense_input, indices, name=None
)

 


 Args
  dense_input   A Tensor. Must be one of the following types: float32, float64, complex64, complex128. A Dense tensor.  
  indices   A Tensor of type int64. Indices of nonzero elements.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.DenseToDenseSetOperation Applies set operation along last dimension of 2 Tensor inputs.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseToDenseSetOperation  
tf.raw_ops.DenseToDenseSetOperation(
    set1, set2, set_operation, validate_indices=True, name=None
)
 See SetOperationOp::SetOperationFromContext for values of set_operation. Output result is a SparseTensor represented by result_indices, result_values, and result_shape. For set1 and set2 ranked n, this has rank n and the same 1st n-1 dimensions as set1 and set2. The nth dimension contains the result of set_operation applied to the corresponding [0...n-1] dimension of set.
 


 Args
  set1   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, string. Tensor with rank n. 1st n-1 dimensions must be the same as set2. Dimension n contains values in a set, duplicates are allowed but ignored.  
  set2   A Tensor. Must have the same type as set1. Tensor with rank n. 1st n-1 dimensions must be the same as set1. Dimension n contains values in a set, duplicates are allowed but ignored.  
  set_operation   A string.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (result_indices, result_values, result_shape).     result_indices   A Tensor of type int64.  
  result_values   A Tensor. Has the same type as set1.  
  result_shape   A Tensor of type int64.
tf.raw_ops.DenseToSparseBatchDataset Creates a dataset that batches input elements into a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseToSparseBatchDataset  
tf.raw_ops.DenseToSparseBatchDataset(
    input_dataset, batch_size, row_shape, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant. A handle to an input dataset. Must have a single component.  
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.  
  row_shape   A Tensor of type int64. A vector representing the dense shape of each row in the produced SparseTensor. The shape may be partially specified, using -1 to indicate that a particular dimension should use the maximum size of all batch elements.  
  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.DenseToSparseSetOperation Applies set operation along last dimension of Tensor and SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DenseToSparseSetOperation  
tf.raw_ops.DenseToSparseSetOperation(
    set1, set2_indices, set2_values, set2_shape, set_operation,
    validate_indices=True, name=None
)
 See SetOperationOp::SetOperationFromContext for values of set_operation. Input set2 is a SparseTensor represented by set2_indices, set2_values, and set2_shape. For set2 ranked n, 1st n-1 dimensions must be the same as set1. Dimension n contains values in a set, duplicates are allowed but ignored. If validate_indices is True, this op validates the order and range of set2 indices. Output result is a SparseTensor represented by result_indices, result_values, and result_shape. For set1 and set2 ranked n, this has rank n and the same 1st n-1 dimensions as set1 and set2. The nth dimension contains the result of set_operation applied to the corresponding [0...n-1] dimension of set.
 


 Args
  set1   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, string. Tensor with rank n. 1st n-1 dimensions must be the same as set2. Dimension n contains values in a set, duplicates are allowed but ignored.  
  set2_indices   A Tensor of type int64. 2D Tensor, indices of a SparseTensor. Must be in row-major order.  
  set2_values   A Tensor. Must have the same type as set1. 1D Tensor, values of a SparseTensor. Must be in row-major order.  
  set2_shape   A Tensor of type int64. 1D Tensor, shape of a SparseTensor. set2_shape[0...n-1] must be the same as the 1st n-1 dimensions of set1, result_shape[n] is the max set size across n-1 dimensions.  
  set_operation   A string.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (result_indices, result_values, result_shape).     result_indices   A Tensor of type int64.  
  result_values   A Tensor. Has the same type as set1.  
  result_shape   A Tensor of type int64.
tf.raw_ops.DepthwiseConv2dNative Computes a 2-D depthwise convolution given 4-D input and filter tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DepthwiseConv2dNative  
tf.raw_ops.DepthwiseConv2dNative(
    input, filter, strides, padding, explicit_paddings=[],
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)
 Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, channel_multiplier], containing in_channels convolutional filters of depth 1, depthwise_conv2d applies a different filter to each input channel (expanding from 1 channel to channel_multiplier channels for each), then concatenates the results together. Thus, the output has in_channels * channel_multiplier channels. for k in 0..in_channels-1
  for q in 0..channel_multiplier-1
    output[b, i, j, k * channel_multiplier + q] =
      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                        filter[di, dj, k, q]
 Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64.  
  filter   A Tensor. Must have the same type as input.  
  strides   A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  explicit_paddings   An optional list of ints. Defaults to [].  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DepthwiseConv2dNativeBackpropFilter  
tf.raw_ops.DepthwiseConv2dNativeBackpropFilter(
    input, filter_sizes, out_backprop, strides, padding, explicit_paddings=[],
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then input is a 4-D [batch, in_height, in_width, in_channels] tensor.  
  filter_sizes   A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, depthwise_multiplier] tensor.  
  out_backprop   A Tensor. Must have the same type as input. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then out_backprop shape is [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input of the convolution.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  explicit_paddings   An optional list of ints. Defaults to [].  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DepthwiseConv2dNativeBackpropInput  
tf.raw_ops.DepthwiseConv2dNativeBackpropInput(
    input_sizes, filter, out_backprop, strides, padding, explicit_paddings=[],
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)

 


 Args
  input_sizes   A Tensor of type int32. An integer vector representing the shape of input, based on data_format. For example, if data_format is 'NHWC' then input is a 4-D [batch, height, width, channels] tensor.  
  filter   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, depthwise_multiplier].  
  out_backprop   A Tensor. Must have the same type as filter. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then out_backprop shape is [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input of the convolution.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  explicit_paddings   An optional list of ints. Defaults to [].  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as filter.
tf.raw_ops.DeserializeIterator Converts the given variant tensor to an iterator and stores it in the given resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DeserializeIterator  
tf.raw_ops.DeserializeIterator(
    resource_handle, serialized, name=None
)

 


 Args
  resource_handle   A Tensor of type resource. A handle to an iterator resource.  
  serialized   A Tensor of type variant. A variant tensor storing the state of the iterator contained in the resource.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.DeserializeManySparse Deserialize and concatenate SparseTensors from a serialized minibatch.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DeserializeManySparse  
tf.raw_ops.DeserializeManySparse(
    serialized_sparse, dtype, name=None
)
 The input serialized_sparse must be a string matrix of shape [N x 3] where N is the minibatch size and the rows correspond to packed outputs of SerializeSparse. The ranks of the original SparseTensor objects must all match. When the final SparseTensor is created, it has rank one higher than the ranks of the incoming SparseTensor objects (they have been concatenated along a new row dimension). The output SparseTensor object's shape values for all dimensions but the first are the max across the input SparseTensor objects' shape values for the corresponding dimensions. Its first shape value is N, the minibatch size. The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run SparseReorder to restore index ordering. For example, if the serialized input is a [2 x 3] matrix representing two original SparseTensor objects: index = [ 0]
        [10]
        [20]
values = [1, 2, 3]
shape = [50]
 and index = [ 2]
        [10]
values = [4, 5]
shape = [30]
 then the final deserialized SparseTensor will be: index = [0  0]
        [0 10]
        [0 20]
        [1  2]
        [1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]

 


 Args
  serialized_sparse   A Tensor of type string. 2-D, The N serialized SparseTensor objects. Must have 3 columns.  
  dtype   A tf.DType. The dtype of the serialized SparseTensor objects.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shape).     sparse_indices   A Tensor of type int64.  
  sparse_values   A Tensor of type dtype.  
  sparse_shape   A Tensor of type int64.
tf.raw_ops.DeserializeSparse Deserialize SparseTensor objects.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DeserializeSparse  
tf.raw_ops.DeserializeSparse(
    serialized_sparse, dtype, name=None
)
 The input serialized_sparse must have the shape [?, ?, ..., ?, 3] where the last dimension stores serialized SparseTensor objects and the other N dimensions (N >= 0) correspond to a batch. The ranks of the original SparseTensor objects must all match. When the final SparseTensor is created, its rank is the rank of the incoming SparseTensor objects plus N; the sparse tensors have been concatenated along new dimensions, one for each batch. The output SparseTensor object's shape values for the original dimensions are the max across the input SparseTensor objects' shape values for the corresponding dimensions. The new dimensions match the size of the batch. The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run SparseReorder to restore index ordering. For example, if the serialized input is a [2 x 3] matrix representing two original SparseTensor objects: index = [ 0]
        [10]
        [20]
values = [1, 2, 3]
shape = [50]
 and index = [ 2]
        [10]
values = [4, 5]
shape = [30]
 then the final deserialized SparseTensor will be: index = [0  0]
        [0 10]
        [0 20]
        [1  2]
        [1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]

 


 Args
  serialized_sparse   A Tensor. Must be one of the following types: string, variant. The serialized SparseTensor objects. The last dimension must have 3 columns.  
  dtype   A tf.DType. The dtype of the serialized SparseTensor objects.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (sparse_indices, sparse_values, sparse_shape).     sparse_indices   A Tensor of type int64.  
  sparse_values   A Tensor of type dtype.  
  sparse_shape   A Tensor of type int64.
tf.raw_ops.DestroyResourceOp Deletes the resource specified by the handle.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DestroyResourceOp  
tf.raw_ops.DestroyResourceOp(
    resource, ignore_lookup_error=True, name=None
)
 All subsequent operations using the resource will result in a NotFound error status.
 


 Args
  resource   A Tensor of type resource. handle to the resource to delete.  
  ignore_lookup_error   An optional bool. Defaults to True. whether to ignore the error when the resource doesn't exist.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.DestroyTemporaryVariable Destroys the temporary variable and returns its final value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DestroyTemporaryVariable  
tf.raw_ops.DestroyTemporaryVariable(
    ref, var_name, name=None
)
 Sets output to the value of the Tensor pointed to by 'ref', then destroys the temporary variable called 'var_name'. All other uses of 'ref' must have executed before this op. This is typically achieved by chaining the ref through each assign op, or by using control dependencies. Outputs the final value of the tensor pointed to by 'ref'.
 


 Args
  ref   A mutable Tensor. A reference to the temporary variable tensor.  
  var_name   A string. Name of the temporary variable, usually the name of the matching 'TemporaryVariable' op.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as ref.
tf.raw_ops.DeviceIndex Return the index of device the op runs.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DeviceIndex  
tf.raw_ops.DeviceIndex(
    device_names, name=None
)
 Given a list of device names, this operation returns the index of the device this op runs. The length of the list is returned in two cases: (1) Device does not exist in the given device list. (2) It is in XLA compilation.
 


 Args
  device_names   A list of strings.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.Diag Returns a diagonal tensor with a given diagonal values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Diag  
tf.raw_ops.Diag(
    diagonal, name=None
)
 Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik] and 0 everywhere else. For example: # 'diagonal' is [1, 2, 3, 4]
tf.diag(diagonal) ==> [[1, 0, 0, 0]
                       [0, 2, 0, 0]
                       [0, 0, 3, 0]
                       [0, 0, 0, 4]]

 


 Args
  diagonal   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128. Rank k tensor where k is at most 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as diagonal.
tf.raw_ops.DiagPart Returns the diagonal part of the tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.DiagPart  
tf.raw_ops.DiagPart(
    input, name=None
)
 This operation returns a tensor with the diagonal part of the input. The diagonal part is computed as follows: Assume input has dimensions [D1,..., Dk, D1,..., Dk], then the output is a tensor of rank k with dimensions [D1,..., Dk] where: diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]. For example: # 'input' is [[1, 0, 0, 0]
              [0, 2, 0, 0]
              [0, 0, 3, 0]
              [0, 0, 0, 4]]

tf.diag_part(input) ==> [1, 2, 3, 4]

 


 Args
  input   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128. Rank k tensor where k is even and not zero.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Digamma Computes Psi, the derivative of Lgamma (the log of the absolute value of  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Digamma  
tf.raw_ops.Digamma(
    x, name=None
)
 Gamma(x)), element-wise.
 


 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.