neulab/docprompting-conala huggingface dataset:
tf.raw_ops.CropAndResize Extracts crops from the input image tensor and resizes them.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResize  
tf.raw_ops.CropAndResize(
    image, boxes, box_ind, crop_size, method='bilinear',
    extrapolation_value=0, name=None
)
 Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned. In particular, if boxes = [[0, 0, 1, 1]], the method will give identical results to using tf.image.resize_bilinear() or tf.image.resize_nearest_neighbor()(depends on the method argument) with align_corners=True.
 


 Args
  image   A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.  
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to extrapolate the input image values.  
  box_ind   A Tensor of type int32. A 1-D tensor of shape [num_boxes] with int32 values in [0, batch). The value of box_ind[i] specifies the image that the i-th box refers to.  
  crop_size   A Tensor of type int32. A 1-D tensor of 2 elements, size = [crop_height, crop_width]. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.  
  method   An optional string from: "bilinear", "nearest". Defaults to "bilinear". A string specifying the sampling method for resizing. It can be either "bilinear" or "nearest" and default to "bilinear". Currently two sampling methods are supported: Bilinear and Nearest Neighbor.  
  extrapolation_value   An optional float. Defaults to 0. Value used for extrapolation, when applicable.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.CropAndResizeGradBoxes Computes the gradient of the crop_and_resize op wrt the input boxes tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResizeGradBoxes  
tf.raw_ops.CropAndResizeGradBoxes(
    grads, image, boxes, box_ind, method='bilinear', name=None
)

 


 Args
  grads   A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].  
  image   A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.  
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the[0, 1]range are allowed, in which case we useextrapolation_valueto extrapolate the input image values. </td> </tr><tr> <td>box_ind</td> <td> ATensorof typeint32. A 1-D tensor of shape[num_boxes]with int32 values in[0, batch). The value ofbox_ind[i]specifies the image that thei-th box refers to. </td> </tr><tr> <td>method</td> <td> An optionalstringfrom:"bilinear". Defaults to"bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now. </td> </tr><tr> <td>name`   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.CropAndResizeGradImage Computes the gradient of the crop_and_resize op wrt the input image tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResizeGradImage  
tf.raw_ops.CropAndResizeGradImage(
    grads, boxes, box_ind, image_size, T, method='bilinear', name=None
)

 


 Args
  grads   A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].  
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the[0, 1]range are allowed, in which case we useextrapolation_valueto extrapolate the input image values. </td> </tr><tr> <td>box_ind</td> <td> ATensorof typeint32. A 1-D tensor of shape[num_boxes]with int32 values in[0, batch). The value ofbox_ind[i]specifies the image that thei-th box refers to. </td> </tr><tr> <td>image_size</td> <td> ATensorof typeint32. A 1-D tensor with value[batch, image_height, image_width, depth]containing the original image size. Bothimage_heightandimage_widthneed to be positive. </td> </tr><tr> <td>T</td> <td> A <a href="../../tf/dtypes/DType"><code>tf.DType</code></a> from:tf.float32, tf.half, tf.float64. </td> </tr><tr> <td>method</td> <td> An optionalstringfrom:"bilinear", "nearest". Defaults to"bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now. </td> </tr><tr> <td>name`   A name for the operation (optional).   
 


 Returns   A Tensor of type T.
tf.raw_ops.Cross Compute the pairwise cross product.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cross  
tf.raw_ops.Cross(
    a, b, name=None
)
 a and b must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently.
 


 Args
  a   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. A tensor containing 3-element vectors.  
  b   A Tensor. Must have the same type as a. Another tensor, of same type and shape as a.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.CrossReplicaSum An Op to sum inputs across replicated TPU instances.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CrossReplicaSum  
tf.raw_ops.CrossReplicaSum(
    input, group_assignment, name=None
)
 Each instance supplies its own input. For example, suppose there are 8 TPU instances: [A, B, C, D, E, F, G, H]. Passing group_assignment=[[0,2,4,6],[1,3,5,7]] sets A, C, E, G as group 0, and B, D, F, H as group 1. Thus we get the outputs: [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H].
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, int32, uint32. The local input to the sum.  
  group_assignment   A Tensor of type int32. An int32 tensor with shape [num_groups, num_replicas_per_group]. group_assignment[i] represents the replica ids in the ith subgroup.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CSRSparseMatrixComponents Reads out the CSR components at batch index.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixComponents  
tf.raw_ops.CSRSparseMatrixComponents(
    csr_sparse_matrix, index, type, name=None
)
 This op is meant only for debugging / testing, and its interface is not expected to be stable.
 


 Args
  csr_sparse_matrix   A Tensor of type variant. A batched CSRSparseMatrix.  
  index   A Tensor of type int32. The index in csr_sparse_matrix's batch.  
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (row_ptrs, col_inds, values).     row_ptrs   A Tensor of type int32.  
  col_inds   A Tensor of type int32.  
  values   A Tensor of type type.
tf.raw_ops.CSRSparseMatrixToDense Convert a (possibly batched) CSRSparseMatrix to dense.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixToDense  
tf.raw_ops.CSRSparseMatrixToDense(
    sparse_input, type, name=None
)

 


 Args
  sparse_input   A Tensor of type variant. A batched CSRSparseMatrix.  
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type type.
tf.raw_ops.CSRSparseMatrixToSparseTensor Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixToSparseTensor  
tf.raw_ops.CSRSparseMatrixToSparseTensor(
    sparse_matrix, type, name=None
)

 


 Args
  sparse_matrix   A Tensor of type variant. A (possibly batched) CSRSparseMatrix.  
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (indices, values, dense_shape).     indices   A Tensor of type int64.  
  values   A Tensor of type type.  
  dense_shape   A Tensor of type int64.
tf.raw_ops.CSVDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CSVDataset  
tf.raw_ops.CSVDataset(
    filenames, compression_type, buffer_size, header, field_delim, use_quote_delim,
    na_value, select_cols, record_defaults, output_shapes, name=None
)

 


 Args
  filenames   A Tensor of type string.  
  compression_type   A Tensor of type string.  
  buffer_size   A Tensor of type int64.  
  header   A Tensor of type bool.  
  field_delim   A Tensor of type string.  
  use_quote_delim   A Tensor of type bool.  
  na_value   A Tensor of type string.  
  select_cols   A Tensor of type int64.  
  record_defaults   A list of Tensor objects with types from: float32, float64, int32, int64, string.  
  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.CSVDatasetV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CSVDatasetV2  
tf.raw_ops.CSVDatasetV2(
    filenames, compression_type, buffer_size, header, field_delim, use_quote_delim,
    na_value, select_cols, record_defaults, exclude_cols, output_shapes, name=None
)

 


 Args
  filenames   A Tensor of type string.  
  compression_type   A Tensor of type string.  
  buffer_size   A Tensor of type int64.  
  header   A Tensor of type bool.  
  field_delim   A Tensor of type string.  
  use_quote_delim   A Tensor of type bool.  
  na_value   A Tensor of type string.  
  select_cols   A Tensor of type int64.  
  record_defaults   A list of Tensor objects with types from: float32, float64, int32, int64, string.  
  exclude_cols   A Tensor of type int64.  
  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.CTCBeamSearchDecoder Performs beam search decoding on the logits given in input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CTCBeamSearchDecoder  
tf.raw_ops.CTCBeamSearchDecoder(
    inputs, sequence_length, beam_width, top_paths, merge_repeated=True, name=None
)
 A note about the attribute merge_repeated: For the beam search decoder, this means that if consecutive entries in a beam are the same, only the first of these is emitted. That is, when the top path is "A B B B B", "A B" is returned if merge_repeated = True but "A B B B B" is returned if merge_repeated = False.
 


 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.  
  sequence_length   A Tensor of type int32. A vector containing sequence lengths, size (batch).  
  beam_width   An int that is >= 1. A scalar >= 0 (beam search beam width).  
  top_paths   An int that is >= 1. A scalar >= 0, <= beam_width (controls output size).  
  merge_repeated   An optional bool. Defaults to True. If true, merge repeated classes in output.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (decoded_indices, decoded_values, decoded_shape, log_probability).     decoded_indices   A list of top_paths Tensor objects with type int64.  
  decoded_values   A list of top_paths Tensor objects with type int64.  
  decoded_shape   A list of top_paths Tensor objects with type int64.  
  log_probability   A Tensor. Has the same type as inputs.
tf.raw_ops.CTCGreedyDecoder Performs greedy decoding on the logits given in inputs.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CTCGreedyDecoder  
tf.raw_ops.CTCGreedyDecoder(
    inputs, sequence_length, merge_repeated=False, name=None
)
 A note about the attribute merge_repeated: if enabled, when consecutive logits' maximum indices are the same, only the first of these is emitted. Labeling the blank '*', the sequence "A B B * B B" becomes "A B B" if merge_repeated = True and "A B B B B" if merge_repeated = False. Regardless of the value of merge_repeated, if the maximum index of a given time and batch corresponds to the blank, index (num_classes - 1), no new element is emitted.
 


 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.  
  sequence_length   A Tensor of type int32. A vector containing sequence lengths, size (batch_size).  
  merge_repeated   An optional bool. Defaults to False. If True, merge repeated classes in output.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (decoded_indices, decoded_values, decoded_shape, log_probability).     decoded_indices   A Tensor of type int64.  
  decoded_values   A Tensor of type int64.  
  decoded_shape   A Tensor of type int64.  
  log_probability   A Tensor. Has the same type as inputs.
tf.raw_ops.CTCLoss Calculates the CTC Loss (log probability) for each batch entry. Also calculates  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CTCLoss  
tf.raw_ops.CTCLoss(
    inputs, labels_indices, labels_values, sequence_length,
    preprocess_collapse_repeated=False, ctc_merge_repeated=True,
    ignore_longer_outputs_than_inputs=False, name=None
)
 the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.
 


 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.  
  labels_indices   A Tensor of type int64. The indices of a SparseTensor<int32, 2>. labels_indices(i, :) == [b, t] means labels_values(i) stores the id for (batch b, time t).  
  labels_values   A Tensor of type int32. The values (labels) associated with the given batch and time.  
  sequence_length   A Tensor of type int32. A vector containing sequence lengths (batch).  
  preprocess_collapse_repeated   An optional bool. Defaults to False. Scalar, if true then repeated labels are collapsed prior to the CTC calculation.  
  ctc_merge_repeated   An optional bool. Defaults to True. Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.  
  ignore_longer_outputs_than_inputs   An optional bool. Defaults to False. Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (loss, gradient).     loss   A Tensor. Has the same type as inputs.  
  gradient   A Tensor. Has the same type as inputs.
tf.raw_ops.CTCLossV2 Calculates the CTC Loss (log probability) for each batch entry. Also calculates  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CTCLossV2  
tf.raw_ops.CTCLossV2(
    inputs, labels_indices, labels_values, sequence_length,
    preprocess_collapse_repeated=False, ctc_merge_repeated=True,
    ignore_longer_outputs_than_inputs=False, name=None
)
 the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.
 


 Args
  inputs   A Tensor of type float32. 3-D, shape: (max_time x batch_size x num_classes), the logits. Default blank label is 0 rather num_classes - 1.  
  labels_indices   A Tensor of type int64. The indices of a SparseTensor<int32, 2>. labels_indices(i, :) == [b, t] means labels_values(i) stores the id for (batch b, time t).  
  labels_values   A Tensor of type int32. The values (labels) associated with the given batch and time.  
  sequence_length   A Tensor of type int32. A vector containing sequence lengths (batch).  
  preprocess_collapse_repeated   An optional bool. Defaults to False. Scalar, if true then repeated labels are collapsed prior to the CTC calculation.  
  ctc_merge_repeated   An optional bool. Defaults to True. Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.  
  ignore_longer_outputs_than_inputs   An optional bool. Defaults to False. Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (loss, gradient).     loss   A Tensor of type float32.  
  gradient   A Tensor of type float32.
tf.raw_ops.CudnnRNN A RNN backed by cuDNN.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNN  
tf.raw_ops.CudnnRNN(
    input, input_h, input_c, params, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, is_training=True, name=None
)
 Computes the RNN from the input and initial states, with respect to the params buffer. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is false.
 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.  
  input_h   A Tensor. Must have the same type as input.  
  input_c   A Tensor. Must have the same type as input.  
  params   A Tensor. Must have the same type as input.  
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".  
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".  
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".  
  dropout   An optional float. Defaults to 0.  
  seed   An optional int. Defaults to 0.  
  seed2   An optional int. Defaults to 0.  
  is_training   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, output_h, output_c, reserve_space).     output   A Tensor. Has the same type as input.  
  output_h   A Tensor. Has the same type as input.  
  output_c   A Tensor. Has the same type as input.  
  reserve_space   A Tensor. Has the same type as input.