docstr: ivy.Container instance method variant of ivy.depthwise_conv2d. This
  method simply wraps the function, and so the docstring for
  ivy.depthwise_conv2d also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input image *[batch_size,h,w,d]*.
  filters
      Convolution filters *[fh,fw,d_in]*. (d_in must be the same as d from self)
  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)
  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
  --------
  >>> a = ivy.randint(0, 255, shape=(1, 128, 128, 3)).astype(ivy.float32) / 255.0
  >>> b = ivy.randint(0, 255, shape=(1, 128, 128, 3)).astype(ivy.float32) / 255.0
  >>> inp = ivy.Container(a=a, b=b)
  >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3])
  >>> y = inp.depthwise_conv2d(filters, 2, 'SAME')
  >>> print(y.shape)
  [1, 64, 64, 3]
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code: def depthwise_conv2d(
      self: ivy.Container,
      filters: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      strides: Union[int, Tuple[int], Tuple[int, int], ivy.Container],
      padding: Union[str, List[int], ivy.Container],
      /,
      *,
      data_format: Union[str, ivy.Container] = "NHWC",
      dilations: Union[
          int, Tuple[int], Tuple[int, int], ivy.Container
      ] = 1,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.depthwise_conv2d. This
      method simply wraps the function, and so the docstring for
      ivy.depthwise_conv2d also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input image *[batch_size,h,w,d]*.
      filters
          Convolution filters *[fh,fw,d_in]*. (d_in must be the same as d from self)
      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)
      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
      --------
      >>> a = ivy.randint(0, 255, shape=(1, 128, 128, 3)).astype(ivy.float32) / 255.0
      >>> b = ivy.randint(0, 255, shape=(1, 128, 128, 3)).astype(ivy.float32) / 255.0
      >>> inp = ivy.Container(a=a, b=b)
      >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3])
      >>> y = inp.depthwise_conv2d(filters, 2, 'SAME')
      >>> print(y.shape)
      [1, 64, 64, 3]
      """
      return self._static_depthwise_conv2d(
          self,
          filters,
          strides,
          padding,
          data_format=data_format,
          dilations=dilations,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          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 24 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj.depthwise_conv2d() and returns: self