docstr: ivy.Array instance 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
  ----------
  self
      Input image *[batch_size,h,w,d_in]* or *[batch_size,d_in,h,w]*.
  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)
  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)
  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.], [2.0],[3.]],
  ...                 [[1.], [2.0],[3.]],
  ...                 [[1.], [2.0],[3.]]]]) #NHWC
  >>> filters = ivy.array([[[[0.]], [[1.]], [[0.]]],
  ...                      [[[0.]], [[1.]], [[0.]]],
  ...                      [[[0.]], [[1.]], [[0.]]]]) #HWIO
  >>> result = x.conv2d(filters, 1, 'SAME', data_format='NHWC',
  ...    dilations= 1)
  >>> print(result)
  ivy.array([[
            [[2.],[4.],[6.]],
            [[3.],[6.],[9.]],
            [[2.],[4.],[6.]]
            ]])
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code: def conv2d(
      self: ivy.Array,
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[int, Tuple[int, int]],
      padding: str,
      /,
      *,
      data_format: str = "NHWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int, int]] = 1,
      dilations: Union[int, Tuple[int, int]] = 1,
      bias: Optional[ivy.Container] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance 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
      ----------
      self
          Input image *[batch_size,h,w,d_in]* or *[batch_size,d_in,h,w]*.
      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)
      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)
      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.], [2.0],[3.]],
      ...                 [[1.], [2.0],[3.]],
      ...                 [[1.], [2.0],[3.]]]]) #NHWC
      >>> filters = ivy.array([[[[0.]], [[1.]], [[0.]]],
      ...                      [[[0.]], [[1.]], [[0.]]],
      ...                      [[[0.]], [[1.]], [[0.]]]]) #HWIO
      >>> result = x.conv2d(filters, 1, 'SAME', data_format='NHWC',
      ...    dilations= 1)
      >>> print(result)
      ivy.array([[
                [[2.],[4.],[6.]],
                [[3.],[6.],[9.]],
                [[2.],[4.],[6.]]
                ]])
      """
      return ivy.conv2d(
          self,
          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 11 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.conv2d() and returns: ivy