*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class
        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):

  // can let binary op to broadcast elements.
  if (elements_depth == 1) {
    return true;
  }

  // In TFLite Conv2D uses OHWI format for filter, and 1HWO for Depthwise Conv.
  // For conv:
  // Check if last dimension in filter equals the first dimension
  // For depthwise conv:
  // Check if the first in filter dimension equals the first dimension.
  if (filter_shape.empty() ||

  // CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
  // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[dq]], %[[cst]])
  // CHECK: return %[[conv]] : tensor<256x8x7x3xf32>
}

// CHECK-LABEL: @fuseMulIntoFullyConnectedWithOptionalAttribute

			return false
		}
	}

	// DefaultLit is necessary for non-constants too: n might be 1.1<<k.
	n = DefaultLit(n, types.Types[types.TINT])
	*np = n

	return true
}

func Conv(n ir.Node, t *types.Type) ir.Node {
	if types.IdenticalStrict(n.Type(), t) {
		return n
	}
	n = ir.NewConvExpr(base.Pos, ir.OCONV, nil, n)
	n.SetType(t)
	n = Expr(n)
	return n
}

      }
      func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) {
        %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
        %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

            (i != out_batch_dim && out_type.isDynamicDim(i))) {
          return false;
        }
      }
    }

    // All ones in "lhs_dilation" means this "mhlo.conv" op should be
    // converted to "tf.Conv2D" or "tf.DepthwiseConv2dNativeOp".
    auto lhs_dilation = conv_op.getLhsDilation().value();
    if (!lhs_dilation.isSplat() || lhs_dilation.getSplatValue<int64_t>() != 1)
      return false;

	if err != nil {
		t.Fatalf("db open conn fail: %v", err)
	}

	conn1, err := db.conn(ctx, cachedOrNewConn)
	if err != nil {
		t.Fatalf("db open conn fail: %v", err)
	}

	conn2, err := db.conn(ctx, cachedOrNewConn)
	if err != nil {
		t.Fatalf("db open conn fail: %v", err)
	}

	if g, w := db.numOpen, 3; g != w {
		t.Errorf("free conns = %d; want %d", g, w)
	}

				conv = conv1
			}
		}
		if Arch.LinkArch.Family == sys.ARM64 || Arch.LinkArch.Family == sys.Wasm || Arch.LinkArch.Family == sys.S390X || s.softFloat {
			if conv1, ok1 := uint64fpConvOpToSSA[twoTypes{s.concreteEtype(ft), s.concreteEtype(tt)}]; ok1 {
				conv = conv1
			}
		}

		if Arch.LinkArch.Family == sys.MIPS && !s.softFloat {
			if ft.Size() == 4 && ft.IsInteger() && !ft.IsSigned() {

	conn1 := <-conns

	// Start another request and grab its connection
	response2c := make(chan string, 1)
	go fetch(2, response2c)
	conn2 := <-conns

	// Send a response on connection 2.
	conn2.(*blockingRemoteAddrConn).addrs <- &net.TCPAddr{
		IP: net.ParseIP("12.12.12.12"), Port: 12}

	// ... and see it
	response2 := <-response2c

    return emitOptionalError(location, "invalid padding format provided");
  }

  // Output always have rank 4. All dimensions are initialized to
  // dynamic size and can be partially inferred.
  // TFL's conv2d is always NHWC format & the filter is OHWI.
  SmallVector<int64_t, 4> return_shape(4, ShapedType::kDynamic);
  return_shape[0] = input_ty.getDimSize(0);
  return_shape[3] = filter_ty.getDimSize(0);