const int64_t dim = GetTensorSpatialDimIndex(num_dims, data_format, i);
      spatial_dim_indices.push_back(dim);

      const int64_t dilation = get_int(dilations[dim]);
      rhs_dilations.push_back(dilation);
      const int64_t stride = get_int(strides[dim]);
      window_strides.push_back(stride);

      Value pad_low, pad_high;

    has_batch_norm = True
    dilations = None
    input_shape = (1, 3, 4, 3)
    filter_shape = (2, 3, 3, 2)
    strides = (1, 1, 1, 1)
    model = self._create_conv2d_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,
        bias_fn,
        activation_fn,
        has_batch_norm,
        strides,
        dilations,
    )

    state.stride_height = height;
    state.stride_width = width;

    if (TFIntListIs1XY1(op, "dilations", &height, &width)) {
      state.dilation_height_factor = height;
      state.dilation_width_factor = width;
    } else {
      // If the 'dilations' attribute is missing, we use the default value (1)
      // for both dilation height and width factor.
      state.dilation_height_factor = intAttrOne;

  int64_t dilations_size = dilations.size();
  if (dilations_size != num_dims)
    return emitOptionalError(
        location, "requires dilations attribute length to be ", num_dims);
  if (llvm::any_of(dilations, is_not_positive))
    return emitOptionalError(location, "requires positive dilations");

  return success();
}

// Verifies that,

      %outputs_8, %control_9 = tf_executor.island wraps "tf.Conv2D"(%arg0, %outputs_0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true} : (tensor<16x224x224x3xf32>, tensor<*xf32>) -> tensor<16x112x112x?xf32>

// CHECK:           %[[VAL_10:.*]] = "tf.Conv2D"(%[[VAL_5]], %[[VAL_9]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<16x32x1x256xbf16>, tensor<1x1x256x256xbf16>) -> tensor<16x32x1x256xbf16>

  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"
    value {
      s: "NHWC"
    }
  }
  attr {
    key: "dilations"
    value {
      list {
        i: 1
        i: 1
        i: 1
        i: 1
      }
    }
  }
  attr {
    key: "explicit_paddings"
    value {
      list {
      }

  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"
    value {
      s: "NHWC"
    }
  }
  attr {
    key: "dilations"
    value {
      list {
        i: 1
        i: 1
        i: 1
        i: 1
      }
    }
  }
  attr {
    key: "explicit_paddings"
    value {
      list {
      }

    // Only 2d pooling is supported in TFLite.
    if (op.getWindowDimensions().size() != 4) {
      return failure();
    }

    // reduce_window op with dilations or padding will supported later.
    // TODO: b/321099943 - Support reduce_window op with dilations and padding.
    if (op.getBaseDilations().has_value() ||
        op.getWindowDilations().has_value() || op.getPadding().has_value()) {
      return failure();
    }

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    %1 = "tf.Relu6"(%0) : (tensor<*xf32>) -> tensor<*xf32>