neulab/docprompting-conala huggingface dataset:
tf.compat.v1.layers.MaxPooling2D Max pooling layer for 2D inputs (e.g. images). Inherits From: MaxPool2D, Layer, Layer, Module 
tf.compat.v1.layers.MaxPooling2D(
    pool_size, strides, padding='valid',
    data_format='channels_last', name=None, **kwargs
)

 


 Arguments
  pool_size   An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   A string. The padding method, either 'valid' or 'same'. Case-insensitive.  
  data_format   A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).  
  name   A string, the name of the layer.   
 


 Attributes
  graph  
 
  scope_name
tf.compat.v1.layers.MaxPooling3D Max pooling layer for 3D inputs (e.g. volumes). Inherits From: MaxPool3D, Layer, Layer, Module 
tf.compat.v1.layers.MaxPooling3D(
    pool_size, strides, padding='valid',
    data_format='channels_last', name=None, **kwargs
)

 


 Arguments
  pool_size   An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   A string. The padding method, either 'valid' or 'same'. Case-insensitive.  
  data_format   A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, height, width).  
  name   A string, the name of the layer.   
 


 Attributes
  graph  
 
  scope_name
tf.compat.v1.layers.max_pooling1d Max Pooling layer for 1D inputs. 
tf.compat.v1.layers.max_pooling1d(
    inputs, pool_size, strides, padding='valid',
    data_format='channels_last', name=None
)

 


 Arguments
  inputs   The tensor over which to pool. Must have rank 3.  
  pool_size   An integer or tuple/list of a single integer, representing the size of the pooling window.  
  strides   An integer or tuple/list of a single integer, specifying the strides of the pooling operation.  
  padding   A string. The padding method, either 'valid' or 'same'. Case-insensitive.  
  data_format   A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length).  
  name   A string, the name of the layer.   
 


 Returns   The output tensor, of rank 3.  

 


 Raises
  ValueError   if eager execution is enabled.
tf.compat.v1.layers.max_pooling2d Max pooling layer for 2D inputs (e.g. images). 
tf.compat.v1.layers.max_pooling2d(
    inputs, pool_size, strides, padding='valid',
    data_format='channels_last', name=None
)

 


 Arguments
  inputs   The tensor over which to pool. Must have rank 4.  
  pool_size   An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   A string. The padding method, either 'valid' or 'same'. Case-insensitive.  
  data_format   A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).  
  name   A string, the name of the layer.   
 


 Returns   Output tensor.  

 


 Raises
  ValueError   if eager execution is enabled.
tf.compat.v1.layers.max_pooling3d Max pooling layer for 3D inputs (e.g. 
tf.compat.v1.layers.max_pooling3d(
    inputs, pool_size, strides, padding='valid',
    data_format='channels_last', name=None
)
 volumes).
 


 Arguments
  inputs   The tensor over which to pool. Must have rank 5.  
  pool_size   An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   A string. The padding method, either 'valid' or 'same'. Case-insensitive.  
  data_format   A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, height, width).  
  name   A string, the name of the layer.   
 


 Returns   Output tensor.  

 


 Raises
  ValueError   if eager execution is enabled.
Module: tf.compat.v1.linalg.experimental Public API for tf.linalg.experimental namespace. Functions conjugate_gradient(...): Conjugate gradient solver.
tf.compat.v1.linalg.l2_normalize Normalizes along dimension axis using an L2 norm. (deprecated arguments)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.math.l2_normalize, tf.compat.v1.nn.l2_normalize  
tf.compat.v1.linalg.l2_normalize(
    x, axis=None, epsilon=1e-12, name=None, dim=None
)
 Warning: SOME ARGUMENTS ARE DEPRECATED: (dim). They will be removed in a future version. Instructions for updating: dim is deprecated, use axis instead For a 1-D tensor with axis = 0, computes output = x / sqrt(max(sum(x**2), epsilon))
 For x with more dimensions, independently normalizes each 1-D slice along dimension axis.
 


 Args
  x   A Tensor.  
  axis   Dimension along which to normalize. A scalar or a vector of integers.  
  epsilon   A lower bound value for the norm. Will use sqrt(epsilon) as the divisor if norm < sqrt(epsilon).  
  name   A name for this operation (optional).  
  dim   Deprecated alias for axis.   
 


 Returns   A Tensor with the same shape as x.
Module: tf.compat.v1.lite Public API for tf.lite namespace. Modules constants module: Public API for tf.lite.constants namespace. experimental module: Public API for tf.lite.experimental namespace. Classes class Interpreter: Interpreter interface for TensorFlow Lite Models. class OpHint: A class that helps build tflite function invocations. class OpsSet: Enum class defining the sets of ops available to generate TFLite models. class Optimize: Enum defining the optimizations to apply when generating tflite graphs. class RepresentativeDataset: Representative dataset to evaluate optimizations. class TFLiteConverter: Convert a TensorFlow model into output_format. class TargetSpec: Specification of target device. class TocoConverter: Convert a TensorFlow model into output_format using TOCO. Functions toco_convert(...): Convert a model using TOCO. (deprecated)
Module: tf.compat.v1.lite.constants Public API for tf.lite.constants namespace.
 


 Other Members
  FLOAT   tf.dtypes.DType  
  FLOAT16   tf.dtypes.DType  
  GRAPHVIZ_DOT   3  
  INT16   tf.dtypes.DType  
  INT32   tf.dtypes.DType  
  INT64   tf.dtypes.DType  
  INT8   tf.dtypes.DType  
  QUANTIZED_UINT8   tf.dtypes.DType  
  STRING   tf.dtypes.DType  
  TFLITE   2
Module: tf.compat.v1.lite.experimental Public API for tf.lite.experimental namespace. Modules nn module: Public API for tf.lite.experimental.nn namespace. Functions convert_op_hints_to_stubs(...): Converts a graphdef with LiteOp hints into stub operations. get_potentially_supported_ops(...): Returns operations potentially supported by TensorFlow Lite. load_delegate(...): Returns loaded Delegate object.
tf.compat.v1.lite.experimental.convert_op_hints_to_stubs Converts a graphdef with LiteOp hints into stub operations. 
tf.compat.v1.lite.experimental.convert_op_hints_to_stubs(
    session=None, graph_def=None, write_callback=(lambda graph_def, comments: None)
)
 This is used to prepare for toco conversion of complex intrinsic usages. Note: only one of session or graph_def should be used, not both.
 


 Args
  session   A TensorFlow session that contains the graph to convert.  
  graph_def   A graph def that we should convert.  
  write_callback   A function pointer that can be used to write intermediate steps of graph transformation (optional).   
 


 Returns   A new graphdef with all ops contained in OpHints being replaced by a single op call with the right parameters.  

 


 Raises
  ValueError   If both session and graph_def are provided.
tf.compat.v1.lite.experimental.get_potentially_supported_ops Returns operations potentially supported by TensorFlow Lite. 
tf.compat.v1.lite.experimental.get_potentially_supported_ops()
 The potentially support list contains a list of ops that are partially or fully supported, which is derived by simply scanning op names to check whether they can be handled without real conversion and specific parameters. Given that some ops may be partially supported, the optimal way to determine if a model's operations are supported is by converting using the TensorFlow Lite converter.
 


 Returns   A list of SupportedOp.
Module: tf.compat.v1.lite.experimental.nn Public API for tf.lite.experimental.nn namespace. Classes class TFLiteLSTMCell: Long short-term memory unit (LSTM) recurrent network cell. class TfLiteRNNCell: The most basic RNN cell. Functions dynamic_rnn(...): Creates a recurrent neural network specified by RNNCell cell.
tf.compat.v1.lite.experimental.nn.TfLiteRNNCell The most basic RNN cell. Inherits From: RNNCell, Layer, Layer, Module 
tf.compat.v1.lite.experimental.nn.TfLiteRNNCell(
    num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs
)
 This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops.
 


 Args
  num_units   int, The number of units in the RNN cell.  
  activation   Nonlinearity to use. Default: tanh. It could also be string that is within Keras activation function names.  
  reuse   (optional) Python boolean describing whether to reuse variables in an existing scope. Raises an error if not True and the existing scope already has the given variables.  
  name   String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.  
  dtype   Default dtype of the layer (default of None means use the type of the first input). Required when build is called before call.  
  **kwargs   Dict, keyword named properties for common layer attributes, like trainable etc when constructing the cell from configs of get_config().   
 


 Raises
  ValueError   If the existing scope already has the given variables.   
 


 Attributes
  graph  
 
  output_size   Integer or TensorShape: size of outputs produced by this cell.  
  scope_name  
 
  state_size   size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. 
   Methods get_initial_state View source 
get_initial_state(
    inputs=None, batch_size=None, dtype=None
)
 zero_state View source 
zero_state(
    batch_size, dtype
)
 Return zero-filled state tensor(s).
 


 Args
  batch_size   int, float, or unit Tensor representing the batch size.  
  dtype   the data type to use for the state.   
 


 Returns   If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.
tf.compat.v1.lite.OpHint.OpHintArgumentTracker Conceptually tracks indices of arguments of "OpHint functions". 
tf.compat.v1.lite.OpHint.OpHintArgumentTracker(
    function_name, unique_function_id, node_name_prefix, attr_name, level=1,
    children_inputs_mappings=None
)
 The inputs and arguments of these functions both use an instance of the class so they can have independent numbering.
 


 Args
  function_name   Name of the function that this tracks arguments for.  
  unique_function_id   UUID of function that this tracks arguments for.  
  node_name_prefix   How identities that are created are named.  
  attr_name   Name of attribute to use to store the index for this hint. i.e. FUNCTION_INPUT_INDEX or FUNCTION_OUTPUT_INDEX  
  level   Hierarchical level of the Ophint node, a number.  
  children_inputs_mappings   Inputs/Outputs mapping for children hints.    Methods add View source 
add(
    arg, tag=None, name=None, aggregate=None, index_override=None
)
 Return a wrapped tensor of an input tensor as an argument.
 


 Args
  arg   A TensorFlow tensor that should be considered an argument.  
  tag   String tag to identify arguments that should be packed.  
  name   Name of argument. This is included in the Identity hint op names.  
  aggregate   Strategy to aggregate. Acceptable values are OpHint.AGGREGATE_FIRST, OpHint.AGGREGATE_LAST, and OpHint.AGGREGATE_STACK. Note, aggregate is only valid if tag is specified.  
  index_override   Specify what input/output index should this be in the final stub. i.e. add(arg0, index=1); add(arg1, index=0) will make the final stub be as stub_func(inputs[arg1, arg0], outputs=[]) rather than the default call order based ordering.   
 


 Returns   A tensor representing the wrapped argument.  

 


 Raises
  ValueError   When indices are not consistent.
tf.compat.v1.lite.TocoConverter Convert a TensorFlow model into output_format using TOCO. This class has been deprecated. Please use lite.TFLiteConverter instead. Methods from_frozen_graph View source 
@classmethod
from_frozen_graph(
    graph_def_file, input_arrays, output_arrays, input_shapes=None
)
 Creates a TocoConverter class from a file containing a frozen graph. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter.from_frozen_graph instead. from_keras_model_file View source 
@classmethod
from_keras_model_file(
    model_file, input_arrays=None, input_shapes=None, output_arrays=None
)
 Creates a TocoConverter class from a tf.keras model file. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter.from_keras_model_file instead. from_saved_model View source 
@classmethod
from_saved_model(
    saved_model_dir, input_arrays=None, input_shapes=None, output_arrays=None,
    tag_set=None, signature_key=None
)
 Creates a TocoConverter class from a SavedModel. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter.from_saved_model instead. from_session View source 
@classmethod
from_session(
    sess, input_tensors, output_tensors
)
 Creates a TocoConverter class from a TensorFlow Session. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter.from_session instead.
tf.compat.v1.lite.toco_convert Convert a model using TOCO. (deprecated) 
tf.compat.v1.lite.toco_convert(
    input_data, input_tensors, output_tensors, *args, **kwargs
)
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter instead. Typically this function is used to convert from TensorFlow GraphDef to TFLite. Conversion can be customized by providing arguments that are forwarded to build_toco_convert_protos (see documentation for details). This function has been deprecated. Please use lite.TFLiteConverter instead.
 


 Args
  input_data   Input data (i.e. often sess.graph_def),  
  input_tensors   List of input tensors. Type and shape are computed using foo.shape and foo.dtype.  
  output_tensors   List of output tensors (only .name is used from this).  
  *args   See build_toco_convert_protos,  
  **kwargs   See build_toco_convert_protos.   
 


 Returns   The converted data. For example if TFLite was the destination, then this will be a tflite flatbuffer in a bytes array.  

 


 Raises   Defined in build_toco_convert_protos.
tf.compat.v1.load_file_system_library Loads a TensorFlow plugin, containing file system implementation. (deprecated) 
tf.compat.v1.load_file_system_library(
    library_filename
)
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.load_library instead. Pass library_filename to a platform-specific mechanism for dynamically loading a library. The rules for determining the exact location of the library are platform-specific and are not documented here.
 


 Args
  library_filename   Path to the plugin. Relative or absolute filesystem path to a dynamic library file.   
 


 Returns   None.  

 


 Raises
  RuntimeError   when unable to load the library.
tf.compat.v1.local_variables Returns local variables. 
tf.compat.v1.local_variables(
    scope=None
)
 Local variables - per process variables, usually not saved/restored to checkpoint and used for temporary or intermediate values. For example, they can be used as counters for metrics computation or number of epochs this machine has read data. The tf.contrib.framework.local_variable() function automatically adds the new variable to GraphKeys.LOCAL_VARIABLES. This convenience function returns the contents of that collection. An alternative to local variables are global variables. See tf.compat.v1.global_variables
 


 Args
  scope   (Optional.) A string. If supplied, the resulting list is filtered to include only items whose name attribute matches scope using re.match. Items without a name attribute are never returned if a scope is supplied. The choice of re.match means that a scope without special tokens filters by prefix.   
 


 Returns   A list of local Variable objects.
tf.compat.v1.local_variables_initializer Returns an Op that initializes all local variables.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.initializers.local_variables  
tf.compat.v1.local_variables_initializer()
 This is just a shortcut for variables_initializer(local_variables())
 


 Returns   An Op that initializes all local variables in the graph.
Module: tf.compat.v1.logging Logging and Summary Operations. Functions TaskLevelStatusMessage(...) debug(...) error(...) fatal(...) flush(...) get_verbosity(...): Return how much logging output will be produced. info(...) log(...) log_every_n(...): Log 'msg % args' at level 'level' once per 'n' times. log_first_n(...): Log 'msg % args' at level 'level' only first 'n' times. log_if(...): Log 'msg % args' at level 'level' only if condition is fulfilled. set_verbosity(...): Sets the threshold for what messages will be logged. vlog(...) warn(...) warning(...)
 


 Other Members
  DEBUG   10  
  ERROR   40  
  FATAL   50  
  INFO   20  
  WARN   30
tf.compat.v1.logging.debug 
tf.compat.v1.logging.debug(
    msg, *args, **kwargs
)
tf.compat.v1.logging.error 
tf.compat.v1.logging.error(
    msg, *args, **kwargs
)
tf.compat.v1.logging.fatal 
tf.compat.v1.logging.fatal(
    msg, *args, **kwargs
)
tf.compat.v1.logging.flush 
tf.compat.v1.logging.flush()
tf.compat.v1.logging.get_verbosity Return how much logging output will be produced. 
tf.compat.v1.logging.get_verbosity()
tf.compat.v1.logging.info 
tf.compat.v1.logging.info(
    msg, *args, **kwargs
)
tf.compat.v1.logging.log 
tf.compat.v1.logging.log(
    level, msg, *args, **kwargs
)
tf.compat.v1.logging.log_every_n Log 'msg % args' at level 'level' once per 'n' times. 
tf.compat.v1.logging.log_every_n(
    level, msg, n, *args
)
 Logs the 1st call, (N+1)st call, (2N+1)st call, etc. Not threadsafe.
 


 Args
  level   The level at which to log.  
  msg   The message to be logged.  
  n   The number of times this should be called before it is logged.  
  *args   The args to be substituted into the msg.
tf.compat.v1.logging.log_first_n Log 'msg % args' at level 'level' only first 'n' times. 
tf.compat.v1.logging.log_first_n(
    level, msg, n, *args
)
 Not threadsafe.
 


 Args
  level   The level at which to log.  
  msg   The message to be logged.  
  n   The number of times this should be called before it is logged.  
  *args   The args to be substituted into the msg.
tf.compat.v1.logging.log_if Log 'msg % args' at level 'level' only if condition is fulfilled. 
tf.compat.v1.logging.log_if(
    level, msg, condition, *args
)
tf.compat.v1.logging.set_verbosity Sets the threshold for what messages will be logged. 
tf.compat.v1.logging.set_verbosity(
    v
)
tf.compat.v1.logging.TaskLevelStatusMessage 
tf.compat.v1.logging.TaskLevelStatusMessage(
    msg
)
tf.compat.v1.logging.vlog 
tf.compat.v1.logging.vlog(
    level, msg, *args, **kwargs
)
tf.compat.v1.logging.warn 
tf.compat.v1.logging.warn(
    msg, *args, **kwargs
)
tf.compat.v1.logging.warning 
tf.compat.v1.logging.warning(
    msg, *args, **kwargs
)
tf.compat.v1.LogMessage A ProtocolMessage
 


 Attributes
  level   Level level  
  message   string message   
 


 Class Variables
  DEBUGGING   10  
  ERROR   40  
  FATAL   50  
  INFO   20  
  Level  
 
  UNKNOWN   0  
  WARN   30
Module: tf.compat.v1.lookup Public API for tf.lookup namespace. Modules experimental module: Public API for tf.lookup.experimental namespace. Classes class KeyValueTensorInitializer: Table initializers given keys and values tensors. class StaticHashTable: A generic hash table that is immutable once initialized. class StaticVocabularyTable: String to Id table that assigns out-of-vocabulary keys to hash buckets. class TextFileIndex: The key and value content to get from each line. class TextFileInitializer: Table initializers from a text file.
Module: tf.compat.v1.lookup.experimental Public API for tf.lookup.experimental namespace. Classes class DatasetInitializer: Creates a table initializer from a tf.data.Dataset. class DenseHashTable: A generic mutable hash table implementation using tensors as backing store.