neulab/docprompting-conala huggingface dataset:
tf.raw_ops.EncodePng PNG-encode an image.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodePng  
tf.raw_ops.EncodePng(
    image, compression=-1, name=None
)
 image is a 3-D uint8 or uint16 Tensor of shape [height, width, channels] where channels is:  1: for grayscale. 2: for grayscale + alpha. 3: for RGB. 4: for RGBA.  The ZLIB compression level, compression, can be -1 for the PNG-encoder default or a value from 0 to 9. 9 is the highest compression level, generating the smallest output, but is slower.
 


 Args
  image   A Tensor. Must be one of the following types: uint8, uint16. 3-D with shape [height, width, channels].  
  compression   An optional int. Defaults to -1. Compression level.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.EncodeProto The op serializes protobuf messages provided in the input tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodeProto  
tf.raw_ops.EncodeProto(
    sizes, values, field_names, message_type,
    descriptor_source='local://', name=None
)
 The types of the tensors in values must match the schema for the fields specified in field_names. All the tensors in values must have a common shape prefix, batch_shape. The sizes tensor specifies repeat counts for each field. The repeat count (last dimension) of a each tensor in values must be greater than or equal to corresponding repeat count in sizes. A message_type name must be provided to give context for the field names. The actual message descriptor can be looked up either in the linked-in descriptor pool or a filename provided by the caller using the descriptor_source attribute. For the most part, the mapping between Proto field types and TensorFlow dtypes is straightforward. However, there are a few special cases:  A proto field that contains a submessage or group can only be converted to DT_STRING (the serialized submessage). This is to reduce the complexity of the API. The resulting string can be used as input to another instance of the decode_proto op. TensorFlow lacks support for unsigned integers. The ops represent uint64 types as a DT_INT64 with the same twos-complement bit pattern (the obvious way). Unsigned int32 values can be represented exactly by specifying type DT_INT64, or using twos-complement if the caller specifies DT_INT32 in the output_types attribute.  The descriptor_source attribute selects the source of protocol descriptors to consult when looking up message_type. This may be:  An empty string or "local://", in which case protocol descriptors are created for C++ (not Python) proto definitions linked to the binary. A file, in which case protocol descriptors are created from the file, which is expected to contain a FileDescriptorSet serialized as a string. NOTE: You can build a descriptor_source file using the --descriptor_set_out and --include_imports options to the protocol compiler protoc. A "bytes://", in which protocol descriptors are created from <bytes>, which is expected to be a FileDescriptorSet serialized as a string. 
 


 Args
  sizes   A Tensor of type int32. Tensor of int32 with shape [batch_shape, len(field_names)].  
  values   A list of Tensor objects. List of tensors containing values for the corresponding field.  
  field_names   A list of strings. List of strings containing proto field names.  
  message_type   A string. Name of the proto message type to decode.  
  descriptor_source   An optional string. Defaults to "local://".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.EncodeWav Encode audio data using the WAV file format.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EncodeWav  
tf.raw_ops.EncodeWav(
    audio, sample_rate, name=None
)
 This operation will generate a string suitable to be saved out to create a .wav audio file. It will be encoded in the 16-bit PCM format. It takes in float values in the range -1.0f to 1.0f, and any outside that value will be clamped to that range. audio is a 2-D float Tensor of shape [length, channels]. sample_rate is a scalar Tensor holding the rate to use (e.g. 44100).
 


 Args
  audio   A Tensor of type float32. 2-D with shape [length, channels].  
  sample_rate   A Tensor of type int32. Scalar containing the sample frequency.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.EnqueueTPUEmbeddingIntegerBatch An op that enqueues a list of input batch tensors to TPUEmbedding.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EnqueueTPUEmbeddingIntegerBatch  
tf.raw_ops.EnqueueTPUEmbeddingIntegerBatch(
    batch, mode_override, device_ordinal=-1, name=None
)

 


 Args
  batch   A list of at least 1 Tensor objects with type int32. A list of 1D tensors, one for each embedding table, containing the indices into the tables.  
  mode_override   A Tensor of type string. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.EnqueueTPUEmbeddingSparseBatch An op that enqueues TPUEmbedding input indices from a SparseTensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EnqueueTPUEmbeddingSparseBatch  
tf.raw_ops.EnqueueTPUEmbeddingSparseBatch(
    sample_indices, embedding_indices, aggregation_weights, mode_override,
    device_ordinal=-1, combiners=[], name=None
)
 This Op eases the porting of code that uses embedding_lookup_sparse(), although some Python preprocessing of the SparseTensor arguments to embedding_lookup_sparse() is required to produce the arguments to this Op, since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training step. The tensors at corresponding positions in the three input lists must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding table_id.
 


 Args
  sample_indices   A list of at least 1 Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors specifying the training example and feature to which the corresponding embedding_indices and aggregation_weights values belong. sample_indices[i] must equal b * nf + f, where nf is the number of features from the corresponding table, f is in [0, nf), and b is in [0, batch size).  
  embedding_indices   A list with the same length as sample_indices of Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors, indices into the embedding tables.  
  aggregation_weights   A list with the same length as sample_indices of Tensor objects with the same type in: float32, float64. A list of rank 1 Tensors containing per sample -- i.e. per (training example, feature) -- aggregation weights.  
  mode_override   A Tensor of type string. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed.  
  combiners   An optional list of strings. Defaults to []. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.EnqueueTPUEmbeddingSparseTensorBatch Eases the porting of code that uses tf.nn.embedding_lookup_sparse().  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EnqueueTPUEmbeddingSparseTensorBatch  
tf.raw_ops.EnqueueTPUEmbeddingSparseTensorBatch(
    sample_indices, embedding_indices, aggregation_weights, mode_override,
    table_ids, device_ordinal=-1, combiners=[], max_sequence_lengths=[], name=None
)
 sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. table_ids[i] indicates which embedding table to look up ith feature. The tensors at corresponding positions in the three input lists (sample_indices, embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding feature.
 


 Args
  sample_indices   A list of at least 1 Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors specifying the training example to which the corresponding embedding_indices and aggregation_weights values belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse().  
  embedding_indices   A list with the same length as sample_indices of Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors, indices into the embedding tables. It corresponds to sp_ids.values in embedding_lookup_sparse().  
  aggregation_weights   A list with the same length as sample_indices of Tensor objects with the same type in: float32, float64. A list of rank 1 Tensors containing per training example aggregation weights. It corresponds to sp_weights.values in embedding_lookup_sparse().  
  mode_override   A Tensor of type string. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used.  
  table_ids   A list of ints. A list of integers specifying the identifier of the embedding table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the corresponding input. The ith input is looked up using table_ids[i]. The size of the table_ids list must be equal to that of sample_indices, embedding_indices and aggregation_weights.  
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed.  
  combiners   An optional list of strings. Defaults to []. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables.  
  max_sequence_lengths   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.EnsureShape Ensures that the tensor's shape matches the expected shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EnsureShape  
tf.raw_ops.EnsureShape(
    input, shape, name=None
)
 Raises an error if the input tensor's shape does not match the specified shape. Returns the input tensor otherwise.
 


 Args
  input   A Tensor. A tensor, whose shape is to be validated.  
  shape   A tf.TensorShape or list of ints. The expected (possibly partially specified) shape of the input tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Enter Creates or finds a child frame, and makes data available to the child frame.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Enter  
tf.raw_ops.Enter(
    data, frame_name, is_constant=False, parallel_iterations=10, name=None
)
 This op is used together with Exit to create loops in the graph. The unique frame_name is used by the Executor to identify frames. If is_constant is true, output is a constant in the child frame; otherwise it may be changed in the child frame. At most parallel_iterations iterations are run in parallel in the child frame.
 


 Args
  data   A Tensor. The tensor to be made available to the child frame.  
  frame_name   A string. The name of the child frame.  
  is_constant   An optional bool. Defaults to False. If true, the output is constant within the child frame.  
  parallel_iterations   An optional int. Defaults to 10. The number of iterations allowed to run in parallel.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.Equal 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.Equal  
tf.raw_ops.Equal(
    x, y, incompatible_shape_error=True, name=None
)
 
Note: Equal supports broadcasting. More about broadcasting here

x = tf.constant([2, 4])
y = tf.constant(2)
tf.math.equal(x, y) ==> array([True, False])

x = tf.constant([2, 4])
y = tf.constant([2, 4])
tf.math.equal(x, y) ==> array([True,  True])

 


 Args
  x   A Tensor.  
  y   A Tensor. Must have the same type as x.  
  incompatible_shape_error   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type bool.
tf.raw_ops.Erf Computes the Gauss error function of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Erf  
tf.raw_ops.Erf(
    x, name=None
)

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Erfc Computes the complementary error function of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Erfc  
tf.raw_ops.Erfc(
    x, name=None
)

 


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


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

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.EuclideanNorm Computes the euclidean norm of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.EuclideanNorm  
tf.raw_ops.EuclideanNorm(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The tensor to reduce.  
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).  
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Exit Exits the current frame to its parent frame.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Exit  
tf.raw_ops.Exit(
    data, name=None
)
 Exit makes its input data available to the parent frame.
 


 Args
  data   A Tensor. The tensor to be made available to the parent frame.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as data.
tf.raw_ops.Exp   Computes exponential of x element-wise. \(y = e^x\).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Exp  
tf.raw_ops.Exp(
    x, name=None
)
 This function computes the exponential of every element in the input tensor. i.e. exp(x) or e^(x), where x is the input tensor. e denotes Euler's number and is approximately equal to 2.718281. Output is positive for any real input. x = tf.constant(2.0)
tf.math.exp(x) ==> 7.389056

x = tf.constant([2.0, 8.0])
tf.math.exp(x) ==> array([7.389056, 2980.958], dtype=float32)
 For complex numbers, the exponential value is calculated as follows: e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y)
 Let's consider complex number 1+1j as an example. e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j) x = tf.constant(1 + 1j)
tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.ExpandDims Inserts a dimension of 1 into a tensor's shape.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExpandDims  
tf.raw_ops.ExpandDims(
    input, axis, name=None
)
 Given a tensor input, this operation inserts a dimension of 1 at the dimension index axis of input's shape. The dimension index axis starts at zero; if you specify a negative number for axis it is counted backward from the end. This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape [height, width, channels], you can make it a batch of 1 image with expand_dims(image, 0), which will make the shape [1, height, width, channels]. Other examples: # 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
 This operation requires that: -1-input.dims() <= dim <= input.dims() This operation is related to squeeze(), which removes dimensions of size 1.
 


 Args
  input   A Tensor.  
  axis   A Tensor. Must be one of the following types: int32, int64. 0-D (scalar). Specifies the dimension index at which to expand the shape of input. Must be in the range [-rank(input) - 1, rank(input)].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.ExperimentalAssertNextDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalAssertNextDataset  
tf.raw_ops.ExperimentalAssertNextDataset(
    input_dataset, transformations, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  transformations   A Tensor of type string.  
  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.ExperimentalAutoShardDataset Creates a dataset that shards the input dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalAutoShardDataset  
tf.raw_ops.ExperimentalAutoShardDataset(
    input_dataset, num_workers, index, output_types, output_shapes,
    auto_shard_policy=0, name=None
)
 Creates a dataset that shards the input dataset by num_workers, returning a sharded dataset for the index-th worker. This attempts to automatically shard a dataset by examining the Dataset graph and inserting a shard op before the inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset). This dataset will throw a NotFound error if we cannot shard the dataset automatically.
 


 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.  
  num_workers   A Tensor of type int64. A scalar representing the number of workers to distribute this dataset across.  
  index   A Tensor of type int64. A scalar representing the index of the current worker out of num_workers.  
  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.  
  auto_shard_policy   An optional int. Defaults to 0.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ExperimentalBytesProducedStatsDataset Records the bytes size of each element of input_dataset in a StatsAggregator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalBytesProducedStatsDataset  
tf.raw_ops.ExperimentalBytesProducedStatsDataset(
    input_dataset, tag, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  tag   A Tensor of type string.  
  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.ExperimentalChooseFastestDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalChooseFastestDataset  
tf.raw_ops.ExperimentalChooseFastestDataset(
    input_datasets, num_experiments, output_types, output_shapes, name=None
)

 


 Args
  input_datasets   A list of at least 2 Tensor objects with type variant.  
  num_experiments   An int.  
  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.ExperimentalCSVDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalCSVDataset  
tf.raw_ops.ExperimentalCSVDataset(
    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.