docstr: ivy.Container static method variant of ivy.conv2d. This method
  simply wraps the function, and so the docstring for ivy.conv2d also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input image *[batch_size,h,w,d_in]*.
  filters
      Convolution filters *[fh,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
      "NHWC" or "NCHW". Defaults to "NHWC".
  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``.
  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.Container(a = ivy.eye(3, 3).reshape((1, 3, 3, 1)),
  ...                   b = ivy.eye(5, 5).reshape((1, 5, 5, 1)))
  >>> filters = ivy.array([[2., 0., 1.],
  ...                      [1., 3., 1.],
  ...                      [0., 1., 1.]]).reshape((3, 3, 1, 1))
  >>> result = ivy.Container.static_conv2d(x, filters, (2,), 'SAME')
  >>> print(result)
  {
      a:ivy.array([[[[4.],[0.]],[[1.],[5.]]]]),
      b:ivy.array([[[[4.],[0.],[0.]],[[1.],[6.],[0.]],[[0.],[1.],[5.]]]])
  }
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code: def _static_conv2d(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      strides: Union[int, Tuple[int, int], ivy.Container],
      padding: Union[str, ivy.Container],
      /,
      *,
      data_format: str = "NHWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int, int]] = 1,
      dilations: Union[int, Tuple[int, 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.conv2d. This method
      simply wraps the function, and so the docstring for ivy.conv2d also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input image *[batch_size,h,w,d_in]*.
      filters
          Convolution filters *[fh,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
          "NHWC" or "NCHW". Defaults to "NHWC".
      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``.
      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.Container(a = ivy.eye(3, 3).reshape((1, 3, 3, 1)),
      ...                   b = ivy.eye(5, 5).reshape((1, 5, 5, 1)))
      >>> filters = ivy.array([[2., 0., 1.],
      ...                      [1., 3., 1.],
      ...                      [0., 1., 1.]]).reshape((3, 3, 1, 1))
      >>> result = ivy.Container.static_conv2d(x, filters, (2,), 'SAME')
      >>> print(result)
      {
          a:ivy.array([[[[4.],[0.]],[[1.],[5.]]]]),
          b:ivy.array([[[[4.],[0.],[0.]],[[1.],[6.],[0.]],[[0.],[1.],[5.]]]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "conv2d",
          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 17 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj._static_conv2d() and returns: ContainerBase