docstr: ivy.Array instance method variant of ivy.conv1d. This method simply
  wraps the function, and so the docstring for ivy.conv1d also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  self
      Input image *[batch_size,w,d_in]* or *[batch_size,d_in,w]*.
  filters
      Convolution filters *[fw,d_in,d_out]*.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      "SAME" or "VALID" indicating the algorithm, or list indicating the
      per-dimension paddings.
  data_format
      "NWC" or "NCW". Defaults to "NWC".
  filter_format
      Either "channel_first" or "channel_last". Defaults to "channel_last".
   x_dilations
      The dilation factor for each dimension of input. (Default value = 1)
  dilations
      The dilation factor for each dimension of input. (Default value = 1)
  bias
      Bias array of shape *[d_out]*.
  out
      optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the convolution operation.
  
  Examples
  --------
  >>> x = ivy.array([[[1., 2.], [3., 4.], [6., 7.], [9., 11.]]])  # NWC
  >>> filters = ivy.array([[[0., 1.], [1., 1.]]])  # WIO (I == C)
  >>> result = x.conv1d(filters, (1,), 'VALID')
  >>> print(result)
  ivy.array([[[ 2.,  3.],
  ...         [ 4.,  7.],
  ...         [ 7., 13.],
  ...         [11., 20.]]])
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code: def conv1d(
      self: ivy.Array,
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[int, Tuple[int]],
      padding: str,
      /,
      *,
      data_format: str = "NWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int]] = 1,
      dilations: Union[int, Tuple[int]] = 1,
      bias: Optional[ivy.Array] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.conv1d. This method simply
      wraps the function, and so the docstring for ivy.conv1d also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      self
          Input image *[batch_size,w,d_in]* or *[batch_size,d_in,w]*.
      filters
          Convolution filters *[fw,d_in,d_out]*.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          "SAME" or "VALID" indicating the algorithm, or list indicating the
          per-dimension paddings.
      data_format
          "NWC" or "NCW". Defaults to "NWC".
      filter_format
          Either "channel_first" or "channel_last". Defaults to "channel_last".
       x_dilations
          The dilation factor for each dimension of input. (Default value = 1)
      dilations
          The dilation factor for each dimension of input. (Default value = 1)
      bias
          Bias array of shape *[d_out]*.
      out
          optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the convolution operation.
  
      Examples
      --------
      >>> x = ivy.array([[[1., 2.], [3., 4.], [6., 7.], [9., 11.]]])  # NWC
      >>> filters = ivy.array([[[0., 1.], [1., 1.]]])  # WIO (I == C)
      >>> result = x.conv1d(filters, (1,), 'VALID')
      >>> print(result)
      ivy.array([[[ 2.,  3.],
      ...         [ 4.,  7.],
      ...         [ 7., 13.],
      ...         [11., 20.]]])
      """
      return ivy.conv1d(
          self._data,
          filters,
          strides,
          padding,
          data_format=data_format,
          filter_format=filter_format,
          x_dilations=x_dilations,
          dilations=dilations,
          bias=bias,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/layers.py
imports: import abc
  from typing import Optional, Tuple, Union, List, Sequence
  import ivy
label_desc: Use image node_id 8 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.conv1d() and returns: ivy