docstr: ivy.Container static 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
  ----------
  x
      Input image *[batch_size,w, d_in]*.
  filters
      Convolution filters *[fw,d_in, d_out]*. (d_in must be the same as d from x)
  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)
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  bias
  Bias array of shape *[d_out]*.
  out
      optional output container, 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.Container(a=ivy.array([[[2., 3., 4.], [5., 6., 7.]]]),
  ...                   b=ivy.array([[[7., 8., 9.], [10., 11., 12]]]))
  >>> filters = ivy.array([[[0., 0.5, 1.], [0.25, 0.5, 0.75], [-0.5, 0., 0.5 ]]])
  >>> result= ivy.Container.static_conv1d(x,filters,(1,),'VALID')
  >>> print(result)
  {
      ... a: ivy.array([[[-1.25, 2.5, 6.25],
      ...                [-2., 5.5, 13.]]]),
      ... b: ivy.array([[[-2.5, 7.5, 17.5],
      ...                [-3.25, 10.5, 24.2]]])
  }
docstr_tok: ["ivy.Container","static","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","--","--","--","--","--","x","Input","image","*","[","batch_size",",","w",",","d_in","]","*",".","filters","Convolution","filters","*","[","fw",",","d_in",",","d_out","]","*",".","(","d_in","must","be","the","same","as","d","from","x",")","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",")","key_chains","The","key-chains","to","apply","or","not","apply","the","method","to",".","Default","is","``","None","``",".","to_apply","If","True",",","the","method","will","be","applied","to","key_chains",",","otherwise","key_chains","will","be","skipped",".","Default","is","``","True","``",".","prune_unapplied","Whether","to","prune","key_chains","for","which","the","function","was","not","applied",".","Default","is","``","False","``",".","map_sequences","Whether","to","also","map","method","to","sequences","(","lists",",","tuples",")",".","Default","is","``","False","``",".","bias","Bias","array","of","shape","*","[","d_out","]","*",".","out","optional","output","container",",","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.Container","(","a=ivy.array","(","[","[","[","2.",",","3.",",","4",".","]",",","[","5.",",","6.",",","7",".","]","]","]",")",",","...","b=ivy.array","(","[","[","[","7.",",","8.",",","9",".","]",",","[","10.",",","11.",",","12","]","]","]",")",")",">",">",">","filters","=","ivy.array","(","[","[","[","0.",",","0.5",",","1",".","]",",","[","0.25",",","0.5",",","0.75","]",",","[","-0.5",",","0.",",","0.5","]","]","]",")",">",">",">","result=","ivy.Container.static_conv1d","(","x",",","filters",",","(","1",",",")",",","'VALID","'",")",">",">",">","print","(","result",")","{","...","a",":","ivy.array","(","[","[","[","-1.25",",","2.5",",","6.25","]",",","...","[","-2.",",","5.5",",","13",".","]","]","]",")",",","...","b",":","ivy.array","(","[","[","[","-2.5",",","7.5",",","17.5","]",",","...","[","-3.25",",","10.5",",","24.2","]","]","]",")","}"]
code: def _static_conv1d(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      strides: Union[int, Tuple[int], ivy.Container],
      padding: Union[str, ivy.Container],
      /,
      *,
      data_format: str = "NWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int]] = 1,
      dilations: Union[int, Tuple[int]] = 1,
      key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
      to_apply: bool = True,
      prune_unapplied: bool = False,
      map_sequences: bool = False,
      bias: Optional[ivy.Container] = None,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static 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
      ----------
      x
          Input image *[batch_size,w, d_in]*.
      filters
          Convolution filters *[fw,d_in, d_out]*. (d_in must be the same as d from x)
      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)
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
      bias
      Bias array of shape *[d_out]*.
      out
          optional output container, 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.Container(a=ivy.array([[[2., 3., 4.], [5., 6., 7.]]]),
      ...                   b=ivy.array([[[7., 8., 9.], [10., 11., 12]]]))
      >>> filters = ivy.array([[[0., 0.5, 1.], [0.25, 0.5, 0.75], [-0.5, 0., 0.5 ]]])
      >>> result= ivy.Container.static_conv1d(x,filters,(1,),'VALID')
      >>> print(result)
      {
          ... a: ivy.array([[[-1.25, 2.5, 6.25],
          ...                [-2., 5.5, 13.]]]),
          ... b: ivy.array([[[-2.5, 7.5, 17.5],
          ...                [-3.25, 10.5, 24.2]]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "conv1d",
          x,
          filters,
          strides,
          padding,
          data_format=data_format,
          filter_format=filter_format,
          x_dilations=x_dilations,
          dilations=dilations,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          bias=bias,
          out=out,
      )
  
imports: from typing import Optional, Tuple, Union, List, Callable, Dict, Sequence
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 15 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj._static_conv1d() and returns: ContainerBase