parameters: (inputs,
             filters,
             kernel_size,
             strides=1,
             padding='valid',
             data_format='channels_last',
             dilation_rate=1,
             activation=None,
             use_bias=True,
             kernel_initializer=None,
             bias_initializer=init_ops.zeros_initializer(),
             kernel_regularizer=None,
             bias_regularizer=None,
             activity_regularizer=None,
             kernel_constraint=None,
             bias_constraint=None,
             trainable=True,
             name=None,
             reuse=None)
return_statement: return layer.apply(inputs)
docstring: Functional interface for 1D convolution layer (e.g. temporal convolution).
  
    This layer creates a convolution kernel that is convolved
    (actually cross-correlated) with the layer input to produce a tensor of
    outputs. If `use_bias` is True (and a `bias_initializer` is provided),
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    Args:
      inputs: Tensor input.
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: An integer or tuple/list of a single integer, specifying the
        length of the 1D convolution window.
      strides: An integer or tuple/list of a single integer,
        specifying the stride length of the convolution.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      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)`.
      dilation_rate: An integer or tuple/list of a single integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      activation: Activation function. Set it to None to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: An initializer for the convolution kernel.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer will be used.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      kernel_constraint: Optional projection function to be applied to the
          kernel after being updated by an `Optimizer` (e.g. used to implement
          norm constraints or value constraints for layer weights). The function
          must take as input the unprojected variable and must return the
          projected variable (which must have the same shape). Constraints are
          not safe to use when doing asynchronous distributed training.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
      trainable: Boolean, if `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: A string, the name of the layer.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
  
    Raises:
      ValueError: if eager execution is enabled.
docstring_summary: Functional interface for 1D convolution layer (e.g. temporal convolution).
docstring_tokens: Functional interface for 1D convolution layer ( e . g . temporal convolution ) .
function: def conv1d(inputs,
             filters,
             kernel_size,
             strides=1,
             padding='valid',
             data_format='channels_last',
             dilation_rate=1,
             activation=None,
             use_bias=True,
             kernel_initializer=None,
             bias_initializer=init_ops.zeros_initializer(),
             kernel_regularizer=None,
             bias_regularizer=None,
             activity_regularizer=None,
             kernel_constraint=None,
             bias_constraint=None,
             trainable=True,
             name=None,
             reuse=None):
    """Functional interface for 1D convolution layer (e.g. temporal convolution).
  
    This layer creates a convolution kernel that is convolved
    (actually cross-correlated) with the layer input to produce a tensor of
    outputs. If `use_bias` is True (and a `bias_initializer` is provided),
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    Args:
      inputs: Tensor input.
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: An integer or tuple/list of a single integer, specifying the
        length of the 1D convolution window.
      strides: An integer or tuple/list of a single integer,
        specifying the stride length of the convolution.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      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)`.
      dilation_rate: An integer or tuple/list of a single integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any `strides` value != 1.
      activation: Activation function. Set it to None to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: An initializer for the convolution kernel.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer will be used.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      kernel_constraint: Optional projection function to be applied to the
          kernel after being updated by an `Optimizer` (e.g. used to implement
          norm constraints or value constraints for layer weights). The function
          must take as input the unprojected variable and must return the
          projected variable (which must have the same shape). Constraints are
          not safe to use when doing asynchronous distributed training.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
      trainable: Boolean, if `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: A string, the name of the layer.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
  
    Raises:
      ValueError: if eager execution is enabled.
    """
    warnings.warn('`tf.layers.conv1d` is deprecated and '
                  'will be removed in a future version. '
                  'Please Use `tf.keras.layers.Conv1D` instead.')
    layer = Conv1D(
        filters=filters,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        dilation_rate=dilation_rate,
        activation=activation,
        use_bias=use_bias,
        kernel_initializer=kernel_initializer,
        bias_initializer=bias_initializer,
        kernel_regularizer=kernel_regularizer,
        bias_regularizer=bias_regularizer,
        activity_regularizer=activity_regularizer,
        kernel_constraint=kernel_constraint,
        bias_constraint=bias_constraint,
        trainable=trainable,
        name=name,
        _reuse=reuse,
        _scope=name)
    return layer.apply(inputs)
function_tokens: def conv1d ( inputs , filters , kernel_size , strides = 1 , padding = 'valid' , data_format = 'channels_last' , dilation_rate = 1 , activation = None , use_bias = True , kernel_initializer = None , bias_initializer = init_ops . zeros_initializer ( ) , kernel_regularizer = None , bias_regularizer = None , activity_regularizer = None , kernel_constraint = None , bias_constraint = None , trainable = True , name = None , reuse = None ) : warnings . warn ( '`tf.layers.conv1d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv1D` instead.' ) layer = Conv1D ( filters = filters , kernel_size = kernel_size , strides = strides , padding = padding , data_format = data_format , dilation_rate = dilation_rate , activation = activation , use_bias = use_bias , kernel_initializer = kernel_initializer , bias_initializer = bias_initializer , kernel_regularizer = kernel_regularizer , bias_regularizer = bias_regularizer , activity_regularizer = activity_regularizer , kernel_constraint = kernel_constraint , bias_constraint = bias_constraint , trainable = trainable , name = name , _reuse = reuse , _scope = name ) return layer . apply ( inputs )