LICENSE placeholder....

LICENSE placeholder....

# OLD: x_y_sum

node: {
  name: "x"
  op: "Placeholder"
  attr: {
    key: "shape"
    value: {
      shape: {
        dim: {
          size: -1
        }
      }
    }
  }
  attr: {
    key: "dtype"
    value: {
      type: DT_INT32
    }
  }
}
node: {
  name: "y"
  op: "Placeholder"
  attr: {
    key: "shape"
    value: {
      shape: {

        conv operation a `tf.Variable`.

    Returns:
      in_placeholder: Input tensor placeholder.
      output_tensor: The resulting tensor of the convolution operation.
    """
    in_placeholder = array_ops.placeholder(dtypes.float32, shape=input_shape)

    filters = random_ops.random_uniform(
        shape=filter_shape, minval=-1.0, maxval=1.0
    )

  }
  return std::nullopt;
}

// Add a key placeholder node to the graph. The key placeholder node will be
// used as input for XlaRecvAtHost/XlaSendFromHost nodes.
absl::StatusOr<Node*> AddHostComputeKeyPlaceholder(
    const string& xla_cluster_name, Graph* g) {
  NodeDef key_def;
  NodeDefBuilder builder(absl::StrCat(xla_cluster_name, "_key_placeholder"),
                         "Placeholder");
  builder.Attr("dtype", DT_STRING);

                      _CONTRIB_WARNING)
# The templated code that replaces the placeholder above sometimes
# sets the __all__ variable. If it does, we have to be sure to add
# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

from tensorflow.python.platform import flags
# The "app" module will be imported as part of the placeholder section above.
_current_module.app.flags = flags  # pylint: disable=undefined-variable

# otherwise, use the data type of the node in the graph.

node {
  name: "p"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {
  name: "x"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }

  *(f_lib_proto.add_function()) = test::function::XTimesTwo();

  // Construct a graph:
  //   A = Placeholder[dtype=int32]
  //   B = XTimesTwo[_tpu_replicate="cluster"](A)
  //   C = XTimesTwo[_xla_compile_id="cluster"](A)
  TF_ASSERT_OK(scope_.graph()->AddFunctionLibrary(f_lib_proto));

  Output x = Placeholder(scope_, DT_FLOAT);
  NameAttrList f;
  f.set_name("XTimesTwo");
  (*f.mutable_attr())["T"].set_type(DT_FLOAT);

# RUN: tf-mlir-translate -graphdef-to-mlir %s

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
}

node {
  name: "func0"
  op: "func_name"
  input: "input"
}

library {
  function {
    signature {
      name: "func_name"
      input_arg {
        name: "arg0"
        type: DT_BOOL
      }
    }
    ret {

  *graph.mutable_library()->add_function() = make_test_fn;
  *graph.add_node() = MakeNode("a", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("b", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("c", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});